[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-AlterLab-IEU-alterlab-timesfm-en":3,"guides-for-AlterLab-IEU-alterlab-timesfm":3856,"similar-k17bkmbgyytbmdsdn8rfyyzwwd86n5h5-en":3857},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":242,"isFallback":226,"parentExtension":248,"providers":249,"relations":254,"repo":256,"tags":3853,"workflow":3854},1778675145461.859,"k17bkmbgyytbmdsdn8rfyyzwwd86n5h5",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Part of the AlterLab Academic Skills suite. Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.",{"claudeCode":12},"AlterLab-IEU/AlterLab-Academic-Skills","TimesFM Forecasting","https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":224,"workflow":240},1778676882391.2727,"kn70ej5n451a9awz83fzeb788n86mt8m","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":205,"promptVersionScoring":206,"purpose":207,"rationale":208,"score":209,"summary":210,"tags":211,"targetMarket":217,"tier":218,"useCases":219},[21,26,29,32,36,39,44,48,51,54,58,62,65,69,72,75,78,81,84,87,90,94,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly identifies the problem of zero-shot time series forecasting and the user intent of using a foundation model without custom training.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill leverages Google's TimesFM foundation model for zero-shot forecasting, offering significant value over default LLM behavior by providing specialized time series capabilities.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension appears production-ready, including a system checker script, clear installation instructions, and examples covering forecasting, visualization, and anomaly detection.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on time series forecasting using TimesFM, with examples demonstrating related tasks like visualization and anomaly detection, all within the scope of time series analysis.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities, including the use of TimesFM, zero-shot forecasting, input formats, and the system checker script.",{"category":40,"check":41,"severity":42,"summary":43},"Invocation","Scoped tools","not_applicable","This check is not applicable as the extension does not expose specific tools but rather uses a model-based approach.",{"category":45,"check":46,"severity":24,"summary":47},"Documentation","Configuration & parameter reference","The API reference documentation details `ForecastConfig` parameters, model loading, and output structures comprehensively.",{"category":33,"check":49,"severity":42,"summary":50},"Tool naming","This check is not applicable as the extension operates via a model interface rather than distinct named tools.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The `forecast` and `forecast_with_covariates` methods accept well-defined inputs (list of arrays, covariate dicts) and return structured outputs (point, quantiles).",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The license is correctly identified as MIT from the bundled LICENSE file, which is a permissive open-source license.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The repository shows recent commits within the last 3 months, indicating active maintenance.",{"category":59,"check":63,"severity":24,"summary":64},"Dependency Management","Dependencies like `torch` and `timesfm` are used, and the `check_system.py` script verifies their installation, implying a managed dependency approach.",{"category":66,"check":67,"severity":42,"summary":68},"Security","Secret Management","The extension does not appear to handle or expose any secrets.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill operates on numerical data and does not appear to load or execute untrusted third-party code or data that could be subject to injection.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","All dependencies are managed via standard Python packaging; there are no runtime downloads or script execution from untrusted remote sources.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill performs local computations and does not appear to modify files outside its designated scope or the user's project folder.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No evidence of detached processes or retry loops around denied tool calls was found.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The extension performs local computations and does not submit any confidential data or undocumented telemetry to third parties.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content is clean and free of hidden text or steering tricks.",{"category":66,"check":88,"severity":24,"summary":89},"Opaque code execution","The bundled code is standard Python and does not contain obfuscated payloads or runtime code fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill assumes standard Python/PyTorch environments and data loading practices, with clear instructions for installation and data preparation.",{"category":95,"check":96,"severity":97,"summary":98},"Trust","Issues Attention","info","There are 2 open issues and 0 closed issues in the last 90 days, indicating low recent activity or a new/stable project.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The skill declares versioning information in its frontmatter, indicating a structured release process.",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","Input data validation is handled by the model's compilation and forecasting processes, which are designed to manage various data shapes and lengths.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The extension is purely analytical and does not perform any destructive operations.",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","The `check_system.py` script and the TimesFM model's internal handling are designed to provide clear error messages and prevent system crashes.",{"category":104,"check":114,"severity":42,"summary":115},"Logging","The extension performs local computations and does not require or implement persistent audit logging.",{"category":117,"check":118,"severity":42,"summary":119},"Compliance","GDPR","The extension operates on global temperature data and does not process personal data.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The extension analyzes global temperature data and has no regional restrictions, making it globally applicable.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The skill requires Python and PyTorch, providing clear installation instructions and system checks for compatibility.",{"category":45,"check":127,"severity":24,"summary":128},"README","A detailed README file is provided, explaining the extension's purpose, usage, and findings.",{"category":33,"check":130,"severity":42,"summary":131},"Tool surface size","This is a model-based skill, not a collection of distinct tools.",{"category":40,"check":133,"severity":42,"summary":134},"Overlapping near-synonym tools","This check is not applicable as the extension does not expose multiple tools with similar names.",{"category":45,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, such as forecasting, prediction intervals, and system checks, are implemented and demonstrated in the examples.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Clear installation instructions and copy-pasteable commands are provided in the SKILL.md and README files.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","The `check_system.py` script and the model's potential errors provide actionable feedback and guidance.",{"category":147,"check":148,"severity":24,"summary":149},"Execution","Pinned dependencies","The project suggests using `uv pip install` and specifies necessary packages, implying dependency management practices.",{"category":33,"check":151,"severity":42,"summary":152},"Dry-run preview","The extension is analytical and does not perform state-changing operations that would require a dry-run mode.",{"category":154,"check":155,"severity":42,"summary":156},"Protocol","Idempotent retry & timeouts","The extension performs local computations and does not involve remote calls or state-changing operations requiring idempotency or timeouts.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","No telemetry is mentioned or implemented; the extension performs local computations.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The purpose is precisely stated: zero-shot time series forecasting with TimesFM for univariate data, without custom model training.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core capability and purpose.",{"category":45,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is well-structured, delegating detailed information to reference files and keeping the main content focused.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Detailed API references and data preparation guides are provided in separate files, following progressive disclosure.",{"category":170,"check":174,"severity":42,"summary":175},"Forked exploration","The skill is not an exploration-intensive task requiring a forked context.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","Multiple comprehensive examples covering basic forecasting, CSV input, anomaly detection, and covariate forecasting are provided and appear functional.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The documentation and examples address potential issues like short series, NaNs, model version compatibility, and batch size tuning.",{"category":104,"check":183,"severity":42,"summary":184},"Tool Fallback","The skill relies on local Python libraries and does not depend on external tools or MCP servers with fallbacks.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The system checker script explicitly halts on critical failures, and the model handles data inconsistencies gracefully.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not implicitly rely on other skills; it only provides clear integration points with libraries like matplotlib.",1778676882277,"This skill enables zero-shot time series forecasting for univariate data using Google's TimesFM foundation model. It supports various input formats and provides probabilistic forecasts with prediction intervals. A crucial system checker script ensures compatibility before model loading.",[195,196,197,198,199],"Zero-shot time series forecasting","Uses Google's TimesFM foundation model","Supports CSV, DataFrame, and array inputs","Provides point forecasts and prediction intervals","Includes system requirements checker script",[201,202,203,204],"Classical statistical models requiring coefficient interpretation","Time series classification or clustering","Multivariate vector autoregression","Tabular data processing (use scikit-learn instead)","3.0.0","4.4.0","To perform time series forecasting on univariate data without custom model training, leveraging a powerful foundation model.","The skill demonstrates excellent documentation, clear examples, and robust system checks, adhering to best practices for usability and maintainability. Minor finding on issue engagement.",96,"High-quality skill for zero-shot time series forecasting using TimesFM with excellent documentation and examples.",[212,213,214,215,216],"forecasting","time-series","foundation-model","python","data-science","global","verified",[220,221,222,223],"Forecasting sales, sensor data, or energy consumption","Predicting stock prices or weather patterns","Analyzing time series data without training custom models","Generating probabilistic forecasts with confidence bands",{"codeQuality":225,"collectedAt":227,"documentation":228,"maintenance":231,"popularity":236,"security":237,"testCoverage":239},{"hasLockfile":226},false,1778676873499,{"descriptionLength":229,"readmeSize":230},389,26497,{"closedIssues90d":8,"forks":8,"hasChangelog":232,"openIssues90d":233,"pushedAt":234,"stars":235},true,2,1777289931000,15,{"npmDownloads":8},{"hasNpmPackage":232,"license":238,"smitheryVerified":226},"MIT",{"hasCi":232,"hasTests":232},{"updatedAt":241},1778676882391,{"basePath":243,"githubOwner":244,"githubRepo":245,"locale":18,"slug":246,"type":247},"skills/data-science/alterlab-timesfm","AlterLab-IEU","AlterLab-Academic-Skills","alterlab-timesfm","skill",null,{"evaluate":250,"extract":252},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":251,"targetMarket":217,"tier":218},[212,213,214,215,216],{"commitSha":253,"license":238},"HEAD",{"repoId":255},"kd7fqvj70pvyn4r3q9kctpnd7d86mfqd",{"_creationTime":257,"_id":255,"identity":258,"providers":259,"workflow":3848},1778675137519.0422,{"githubOwner":244,"githubRepo":245,"sourceUrl":14},{"classify":260,"discover":3829,"extract":3832,"github":3834,"npm":3847},{"commitSha":253,"extensions":261},[262,284,301,323,345,357,369,390,405,422,431,450,460,477,497,538,558,570,583,603,619,633,642,654,677,685,705,721,745,760,774,784,797,807,827,846,865,888,934,999,1009,1019,1043,1074,1092,1110,1155,1165,1227,1247,1275,1343,1369,1379,1389,1409,1433,1449,1467,1489,1510,1536,1563,1586,1606,1621,1643,1663,1683,1701,1719,1775,1795,1811,1820,1837,1846,1855,1866,1876,1887,1902,1912,1923,1934,1945,1958,1970,1986,1996,2016,2025,2037,2061,2077,2087,2099,2108,2117,2127,2153,2163,2172,2184,2193,2203,2218,2233,2242,2255,2267,2278,2290,2308,2330,2339,2390,2409,2431,2460,2492,2515,2523,2552,2574,2594,2604,2624,2641,2679,2698,2717,2727,2756,2776,2800,2822,2837,2847,2871,2885,2907,2932,2951,2973,2995,3008,3025,3039,3057,3072,3094,3109,3128,3146,3154,3164,3174,3193,3213,3244,3254,3264,3275,3285,3304,3314,3324,3342,3361,3441,3459,3477,3504,3517,3527,3560,3579,3606,3626,3643,3653,3667,3696,3708,3748,3773],{"basePath":263,"description":264,"displayName":265,"installMethods":266,"rationale":267,"selectedPaths":268,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-anndata","Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census. Part of the AlterLab Academic Skills suite.","alterlab-anndata",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-anndata/SKILL.md",[269,272,275,277,279,281],{"path":270,"priority":271},"SKILL.md","mandatory",{"path":273,"priority":274},"references/best_practices.md","medium",{"path":276,"priority":274},"references/concatenation.md",{"path":278,"priority":274},"references/data_structure.md",{"path":280,"priority":274},"references/io_operations.md",{"path":282,"priority":274},"references/manipulation.md","rule",{"basePath":285,"description":286,"displayName":287,"installMethods":288,"rationale":289,"selectedPaths":290,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-arboreto","Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. Part of the AlterLab Academic Skills suite.","alterlab-arboreto",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-arboreto/SKILL.md",[291,292,294,296,298],{"path":270,"priority":271},{"path":293,"priority":274},"references/algorithms.md",{"path":295,"priority":274},"references/basic_inference.md",{"path":297,"priority":274},"references/distributed_computing.md",{"path":299,"priority":300},"scripts/basic_grn_inference.py","low",{"basePath":302,"description":303,"displayName":304,"installMethods":305,"rationale":306,"selectedPaths":307,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-biopython","Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices. Part of the AlterLab Academic Skills suite.","alterlab-biopython",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-biopython/SKILL.md",[308,309,311,313,315,317,319,321],{"path":270,"priority":271},{"path":310,"priority":274},"references/advanced.md",{"path":312,"priority":274},"references/alignment.md",{"path":314,"priority":274},"references/blast.md",{"path":316,"priority":274},"references/databases.md",{"path":318,"priority":274},"references/phylogenetics.md",{"path":320,"priority":274},"references/sequence_io.md",{"path":322,"priority":274},"references/structure.md",{"basePath":324,"description":325,"displayName":326,"installMethods":327,"rationale":328,"selectedPaths":329,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-bioservices","Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython. Part of the AlterLab Academic Skills suite.","alterlab-bioservices",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-bioservices/SKILL.md",[330,331,333,335,337,339,341,343],{"path":270,"priority":271},{"path":332,"priority":274},"references/identifier_mapping.md",{"path":334,"priority":274},"references/services_reference.md",{"path":336,"priority":274},"references/workflow_patterns.md",{"path":338,"priority":300},"scripts/batch_id_converter.py",{"path":340,"priority":300},"scripts/compound_cross_reference.py",{"path":342,"priority":300},"scripts/pathway_analysis.py",{"path":344,"priority":300},"scripts/protein_analysis_workflow.py",{"basePath":346,"description":347,"displayName":348,"installMethods":349,"rationale":350,"selectedPaths":351,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-cellxgene","Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools. Part of the AlterLab Academic Skills suite.","alterlab-cellxgene",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-cellxgene/SKILL.md",[352,353,355],{"path":270,"priority":271},{"path":354,"priority":274},"references/census_schema.md",{"path":356,"priority":274},"references/common_patterns.md",{"basePath":358,"description":359,"displayName":360,"installMethods":361,"rationale":362,"selectedPaths":363,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-cobrapy","Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis. Part of the AlterLab Academic Skills suite.","alterlab-cobrapy",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-cobrapy/SKILL.md",[364,365,367],{"path":270,"priority":271},{"path":366,"priority":274},"references/api_quick_reference.md",{"path":368,"priority":274},"references/workflows.md",{"basePath":370,"description":371,"displayName":372,"installMethods":373,"rationale":374,"selectedPaths":375,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-deeptools","NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization. Part of the AlterLab Academic Skills suite.","alterlab-deeptools",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-deeptools/SKILL.md",[376,377,379,381,383,385,386,388],{"path":270,"priority":271},{"path":378,"priority":300},"assets/quick_reference.md",{"path":380,"priority":274},"references/effective_genome_sizes.md",{"path":382,"priority":274},"references/normalization_methods.md",{"path":384,"priority":274},"references/tools_reference.md",{"path":368,"priority":274},{"path":387,"priority":300},"scripts/validate_files.py",{"path":389,"priority":300},"scripts/workflow_generator.py",{"basePath":391,"description":392,"displayName":393,"installMethods":394,"rationale":395,"selectedPaths":396,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-esm","Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference. Part of the AlterLab Academic Skills suite.","alterlab-esm",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-esm/SKILL.md",[397,398,400,402,404],{"path":270,"priority":271},{"path":399,"priority":274},"references/esm-c-api.md",{"path":401,"priority":274},"references/esm3-api.md",{"path":403,"priority":274},"references/forge-api.md",{"path":368,"priority":274},{"basePath":406,"description":407,"displayName":408,"installMethods":409,"rationale":410,"selectedPaths":411,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-etetoolkit","Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics. Part of the AlterLab Academic Skills suite.","alterlab-etetoolkit",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-etetoolkit/SKILL.md",[412,413,415,417,418,420],{"path":270,"priority":271},{"path":414,"priority":274},"references/api_reference.md",{"path":416,"priority":274},"references/visualization.md",{"path":368,"priority":274},{"path":419,"priority":300},"scripts/quick_visualize.py",{"path":421,"priority":300},"scripts/tree_operations.py",{"basePath":423,"description":424,"displayName":425,"installMethods":426,"rationale":427,"selectedPaths":428,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-flowio","Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing. Part of the AlterLab Academic Skills suite.","alterlab-flowio",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-flowio/SKILL.md",[429,430],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":432,"description":433,"displayName":434,"installMethods":435,"rationale":436,"selectedPaths":437,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-gget","Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices. Part of the AlterLab Academic Skills suite.","alterlab-gget",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-gget/SKILL.md",[438,439,441,443,444,446,448],{"path":270,"priority":271},{"path":440,"priority":274},"references/database_info.md",{"path":442,"priority":274},"references/module_reference.md",{"path":368,"priority":274},{"path":445,"priority":300},"scripts/batch_sequence_analysis.py",{"path":447,"priority":300},"scripts/enrichment_pipeline.py",{"path":449,"priority":300},"scripts/gene_analysis.py",{"basePath":451,"description":452,"displayName":453,"installMethods":454,"rationale":455,"selectedPaths":456,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-glycoengineering","Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design. Part of the AlterLab Academic Skills suite.","alterlab-glycoengineering",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-glycoengineering/SKILL.md",[457,458],{"path":270,"priority":271},{"path":459,"priority":274},"references/glycan_databases.md",{"basePath":461,"description":462,"displayName":463,"installMethods":464,"rationale":465,"selectedPaths":466,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-histolab","Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml. Part of the AlterLab Academic Skills suite.","alterlab-histolab",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-histolab/SKILL.md",[467,468,470,472,474,476],{"path":270,"priority":271},{"path":469,"priority":274},"references/filters_preprocessing.md",{"path":471,"priority":274},"references/slide_management.md",{"path":473,"priority":274},"references/tile_extraction.md",{"path":475,"priority":274},"references/tissue_masks.md",{"path":416,"priority":274},{"basePath":478,"description":479,"displayName":480,"installMethods":481,"rationale":482,"selectedPaths":483,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-lamindb","This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies. Part of the AlterLab Academic Skills suite.","alterlab-lamindb",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-lamindb/SKILL.md",[484,485,487,489,491,493,495],{"path":270,"priority":271},{"path":486,"priority":274},"references/annotation-validation.md",{"path":488,"priority":274},"references/core-concepts.md",{"path":490,"priority":274},"references/data-management.md",{"path":492,"priority":274},"references/integrations.md",{"path":494,"priority":274},"references/ontologies.md",{"path":496,"priority":274},"references/setup-deployment.md",{"basePath":498,"description":499,"displayName":500,"installMethods":501,"rationale":502,"selectedPaths":503,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-neuropixels","Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation. Part of the AlterLab Academic Skills suite.","alterlab-neuropixels",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-neuropixels/SKILL.md",[504,505,507,509,511,513,515,517,519,521,522,524,526,528,530,532,534,536],{"path":270,"priority":271},{"path":506,"priority":300},"assets/analysis_template.py",{"path":508,"priority":274},"references/AI_CURATION.md",{"path":510,"priority":274},"references/ANALYSIS.md",{"path":512,"priority":274},"references/AUTOMATED_CURATION.md",{"path":514,"priority":274},"references/MOTION_CORRECTION.md",{"path":516,"priority":274},"references/PREPROCESSING.md",{"path":518,"priority":274},"references/QUALITY_METRICS.md",{"path":520,"priority":274},"references/SPIKE_SORTING.md",{"path":414,"priority":274},{"path":523,"priority":274},"references/plotting_guide.md",{"path":525,"priority":274},"references/standard_workflow.md",{"path":527,"priority":300},"scripts/compute_metrics.py",{"path":529,"priority":300},"scripts/explore_recording.py",{"path":531,"priority":300},"scripts/export_to_phy.py",{"path":533,"priority":300},"scripts/neuropixels_pipeline.py",{"path":535,"priority":300},"scripts/preprocess_recording.py",{"path":537,"priority":300},"scripts/run_sorting.py",{"basePath":539,"description":540,"displayName":541,"installMethods":542,"rationale":543,"selectedPaths":544,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-pathml","Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler. Part of the AlterLab Academic Skills suite.","alterlab-pathml",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-pathml/SKILL.md",[545,546,548,550,552,554,556],{"path":270,"priority":271},{"path":547,"priority":274},"references/data_management.md",{"path":549,"priority":274},"references/graphs.md",{"path":551,"priority":274},"references/image_loading.md",{"path":553,"priority":274},"references/machine_learning.md",{"path":555,"priority":274},"references/multiparametric.md",{"path":557,"priority":274},"references/preprocessing.md",{"basePath":559,"description":560,"displayName":561,"installMethods":562,"rationale":563,"selectedPaths":564,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-phylogenetics","Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies. Part of the AlterLab Academic Skills suite.","alterlab-phylogenetics",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-phylogenetics/SKILL.md",[565,566,568],{"path":270,"priority":271},{"path":567,"priority":274},"references/iqtree_inference.md",{"path":569,"priority":300},"scripts/phylogenetic_analysis.py",{"basePath":571,"description":572,"displayName":573,"installMethods":574,"rationale":575,"selectedPaths":576,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-pydeseq2","Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis. Part of the AlterLab Academic Skills suite.","alterlab-pydeseq2",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-pydeseq2/SKILL.md",[577,578,579,581],{"path":270,"priority":271},{"path":414,"priority":274},{"path":580,"priority":274},"references/workflow_guide.md",{"path":582,"priority":300},"scripts/run_deseq2_analysis.py",{"basePath":584,"description":585,"displayName":586,"installMethods":587,"rationale":588,"selectedPaths":589,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-pyopenms","Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms. Part of the AlterLab Academic Skills suite.","alterlab-pyopenms",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-pyopenms/SKILL.md",[590,591,593,595,597,599,601],{"path":270,"priority":271},{"path":592,"priority":274},"references/data_structures.md",{"path":594,"priority":274},"references/feature_detection.md",{"path":596,"priority":274},"references/file_io.md",{"path":598,"priority":274},"references/identification.md",{"path":600,"priority":274},"references/metabolomics.md",{"path":602,"priority":274},"references/signal_processing.md",{"basePath":604,"description":605,"displayName":606,"installMethods":607,"rationale":608,"selectedPaths":609,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-pysam","Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines. Part of the AlterLab Academic Skills suite.","alterlab-pysam",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-pysam/SKILL.md",[610,611,613,615,617],{"path":270,"priority":271},{"path":612,"priority":274},"references/alignment_files.md",{"path":614,"priority":274},"references/common_workflows.md",{"path":616,"priority":274},"references/sequence_files.md",{"path":618,"priority":274},"references/variant_files.md",{"basePath":620,"description":621,"displayName":622,"installMethods":623,"rationale":624,"selectedPaths":625,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-scanpy","Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata. Part of the AlterLab Academic Skills suite.","alterlab-scanpy",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-scanpy/SKILL.md",[626,627,628,629,630,631],{"path":270,"priority":271},{"path":506,"priority":300},{"path":414,"priority":274},{"path":523,"priority":274},{"path":525,"priority":274},{"path":632,"priority":300},"scripts/qc_analysis.py",{"basePath":634,"description":635,"displayName":636,"installMethods":637,"rationale":638,"selectedPaths":639,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-scikit-bio","Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis. Part of the AlterLab Academic Skills suite.","alterlab-scikit-bio",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-scikit-bio/SKILL.md",[640,641],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":643,"description":644,"displayName":645,"installMethods":646,"rationale":647,"selectedPaths":648,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-scvelo","RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference. Part of the AlterLab Academic Skills suite.","alterlab-scvelo",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-scvelo/SKILL.md",[649,650,652],{"path":270,"priority":271},{"path":651,"priority":274},"references/velocity_models.md",{"path":653,"priority":300},"scripts/rna_velocity_workflow.py",{"basePath":655,"description":656,"displayName":657,"installMethods":658,"rationale":659,"selectedPaths":660,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-scvi-tools","Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy. Part of the AlterLab Academic Skills suite.","alterlab-scvi-tools",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-scvi-tools/SKILL.md",[661,662,664,666,668,670,672,674,676],{"path":270,"priority":271},{"path":663,"priority":274},"references/differential-expression.md",{"path":665,"priority":274},"references/models-atac-seq.md",{"path":667,"priority":274},"references/models-multimodal.md",{"path":669,"priority":274},"references/models-scrna-seq.md",{"path":671,"priority":274},"references/models-spatial.md",{"path":673,"priority":274},"references/models-specialized.md",{"path":675,"priority":274},"references/theoretical-foundations.md",{"path":368,"priority":274},{"basePath":678,"description":679,"displayName":680,"installMethods":681,"rationale":682,"selectedPaths":683,"source":283,"sourceLanguage":18,"type":247},"skills/bioinformatics/alterlab-tiledbvcf","Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics. Part of the AlterLab Academic Skills suite.","alterlab-tiledbvcf",{"claudeCode":12},"SKILL.md frontmatter at skills/bioinformatics/alterlab-tiledbvcf/SKILL.md",[684],{"path":270,"priority":271},{"basePath":686,"description":687,"displayName":688,"installMethods":689,"rationale":690,"selectedPaths":691,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-datamol","Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly. Part of the AlterLab Academic Skills suite.","alterlab-datamol",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-datamol/SKILL.md",[692,693,695,697,699,701,703],{"path":270,"priority":271},{"path":694,"priority":274},"references/conformers_module.md",{"path":696,"priority":274},"references/core_api.md",{"path":698,"priority":274},"references/descriptors_viz.md",{"path":700,"priority":274},"references/fragments_scaffolds.md",{"path":702,"priority":274},"references/io_module.md",{"path":704,"priority":274},"references/reactions_data.md",{"basePath":706,"description":707,"displayName":708,"installMethods":709,"rationale":710,"selectedPaths":711,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-deepchem","Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc. Part of the AlterLab Academic Skills suite.","alterlab-deepchem",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-deepchem/SKILL.md",[712,713,714,715,717,719],{"path":270,"priority":271},{"path":414,"priority":274},{"path":368,"priority":274},{"path":716,"priority":300},"scripts/graph_neural_network.py",{"path":718,"priority":300},"scripts/predict_solubility.py",{"path":720,"priority":300},"scripts/transfer_learning.py",{"basePath":722,"description":723,"displayName":724,"installMethods":725,"rationale":726,"selectedPaths":727,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-diffdock","Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction. Part of the AlterLab Academic Skills suite.","alterlab-diffdock",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-diffdock/SKILL.md",[728,729,731,733,735,737,739,741,743],{"path":270,"priority":271},{"path":730,"priority":300},"assets/batch_template.csv",{"path":732,"priority":300},"assets/custom_inference_config.yaml",{"path":734,"priority":274},"references/confidence_and_limitations.md",{"path":736,"priority":274},"references/parameters_reference.md",{"path":738,"priority":274},"references/workflows_examples.md",{"path":740,"priority":300},"scripts/analyze_results.py",{"path":742,"priority":300},"scripts/prepare_batch_csv.py",{"path":744,"priority":300},"scripts/setup_check.py",{"basePath":746,"description":747,"displayName":748,"installMethods":749,"rationale":750,"selectedPaths":751,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-matchms","Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.","alterlab-matchms",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-matchms/SKILL.md",[752,753,755,757,759],{"path":270,"priority":271},{"path":754,"priority":274},"references/filtering.md",{"path":756,"priority":274},"references/importing_exporting.md",{"path":758,"priority":274},"references/similarity.md",{"path":368,"priority":274},{"basePath":761,"description":762,"displayName":763,"installMethods":764,"rationale":765,"selectedPaths":766,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-medchem","Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. Part of the AlterLab Academic Skills suite.","alterlab-medchem",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-medchem/SKILL.md",[767,768,770,772],{"path":270,"priority":271},{"path":769,"priority":274},"references/api_guide.md",{"path":771,"priority":274},"references/rules_catalog.md",{"path":773,"priority":300},"scripts/filter_molecules.py",{"basePath":775,"description":776,"displayName":777,"installMethods":778,"rationale":779,"selectedPaths":780,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-molecular-dynamics","Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics. Part of the AlterLab Academic Skills suite.","alterlab-molecular-dynamics",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-molecular-dynamics/SKILL.md",[781,782],{"path":270,"priority":271},{"path":783,"priority":274},"references/mdanalysis_analysis.md",{"basePath":785,"description":786,"displayName":787,"installMethods":788,"rationale":789,"selectedPaths":790,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-molfeat","Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML. Part of the AlterLab Academic Skills suite.","alterlab-molfeat",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-molfeat/SKILL.md",[791,792,793,795],{"path":270,"priority":271},{"path":414,"priority":274},{"path":794,"priority":274},"references/available_featurizers.md",{"path":796,"priority":274},"references/examples.md",{"basePath":798,"description":799,"displayName":800,"installMethods":801,"rationale":802,"selectedPaths":803,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-primekg","Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more. Part of the AlterLab Academic Skills suite.","alterlab-primekg",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-primekg/SKILL.md",[804,805],{"path":270,"priority":271},{"path":806,"priority":300},"scripts/query_primekg.py",{"basePath":808,"description":809,"displayName":810,"installMethods":811,"rationale":812,"selectedPaths":813,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-pytdc","Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction. Part of the AlterLab Academic Skills suite.","alterlab-pytdc",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-pytdc/SKILL.md",[814,815,817,819,821,823,825],{"path":270,"priority":271},{"path":816,"priority":274},"references/datasets.md",{"path":818,"priority":274},"references/oracles.md",{"path":820,"priority":274},"references/utilities.md",{"path":822,"priority":300},"scripts/benchmark_evaluation.py",{"path":824,"priority":300},"scripts/load_and_split_data.py",{"path":826,"priority":300},"scripts/molecular_generation.py",{"basePath":828,"description":829,"displayName":830,"installMethods":831,"rationale":832,"selectedPaths":833,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-rdkit","Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms. Part of the AlterLab Academic Skills suite.","alterlab-rdkit",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-rdkit/SKILL.md",[834,835,836,838,840,842,844],{"path":270,"priority":271},{"path":414,"priority":274},{"path":837,"priority":274},"references/descriptors_reference.md",{"path":839,"priority":274},"references/smarts_patterns.md",{"path":841,"priority":300},"scripts/molecular_properties.py",{"path":843,"priority":300},"scripts/similarity_search.py",{"path":845,"priority":300},"scripts/substructure_filter.py",{"basePath":847,"description":848,"displayName":849,"installMethods":850,"rationale":851,"selectedPaths":852,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-rowan","Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required. Part of the AlterLab Academic Skills suite.","alterlab-rowan",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-rowan/SKILL.md",[853,854,855,857,859,861,863],{"path":270,"priority":271},{"path":414,"priority":274},{"path":856,"priority":274},"references/molecule_handling.md",{"path":858,"priority":274},"references/proteins_and_organization.md",{"path":860,"priority":274},"references/rdkit_native.md",{"path":862,"priority":274},"references/results_interpretation.md",{"path":864,"priority":274},"references/workflow_types.md",{"basePath":866,"description":867,"displayName":868,"installMethods":869,"rationale":870,"selectedPaths":871,"source":283,"sourceLanguage":18,"type":247},"skills/cheminformatics/alterlab-torchdrug","PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc. Part of the AlterLab Academic Skills suite.","alterlab-torchdrug",{"claudeCode":12},"SKILL.md frontmatter at skills/cheminformatics/alterlab-torchdrug/SKILL.md",[872,873,875,876,878,880,882,884,886],{"path":270,"priority":271},{"path":874,"priority":274},"references/core_concepts.md",{"path":816,"priority":274},{"path":877,"priority":274},"references/knowledge_graphs.md",{"path":879,"priority":274},"references/models_architectures.md",{"path":881,"priority":274},"references/molecular_generation.md",{"path":883,"priority":274},"references/molecular_property_prediction.md",{"path":885,"priority":274},"references/protein_modeling.md",{"path":887,"priority":274},"references/retrosynthesis.md",{"basePath":889,"description":890,"displayName":891,"installMethods":892,"rationale":893,"selectedPaths":894,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-clinical-decision","Part of the AlterLab Academic Skills suite. Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.","alterlab-clinical-decision",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-clinical-decision/SKILL.md",[895,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932],{"path":270,"priority":271},{"path":897,"priority":300},"assets/biomarker_report_template.tex",{"path":899,"priority":300},"assets/clinical_pathway_template.tex",{"path":901,"priority":300},"assets/cohort_analysis_template.tex",{"path":903,"priority":300},"assets/color_schemes.tex",{"path":905,"priority":300},"assets/example_gbm_cohort.md",{"path":907,"priority":300},"assets/recommendation_strength_guide.md",{"path":909,"priority":300},"assets/treatment_recommendation_template.tex",{"path":911,"priority":274},"references/README.md",{"path":913,"priority":274},"references/biomarker_classification.md",{"path":915,"priority":274},"references/clinical_decision_algorithms.md",{"path":917,"priority":274},"references/evidence_synthesis.md",{"path":919,"priority":274},"references/outcome_analysis.md",{"path":921,"priority":274},"references/patient_cohort_analysis.md",{"path":923,"priority":274},"references/treatment_recommendations.md",{"path":925,"priority":300},"scripts/biomarker_classifier.py",{"path":927,"priority":300},"scripts/build_decision_tree.py",{"path":929,"priority":300},"scripts/create_cohort_tables.py",{"path":931,"priority":300},"scripts/generate_survival_analysis.py",{"path":933,"priority":300},"scripts/validate_cds_document.py",{"basePath":935,"description":936,"displayName":937,"installMethods":938,"rationale":939,"selectedPaths":940,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-clinical-reports","Part of the AlterLab Academic Skills suite. Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools.","alterlab-clinical-reports",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-clinical-reports/SKILL.md",[941,942,944,946,948,950,952,954,956,958,960,962,964,966,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997],{"path":270,"priority":271},{"path":943,"priority":300},"assets/case_report_template.md",{"path":945,"priority":300},"assets/clinical_trial_csr_template.md",{"path":947,"priority":300},"assets/clinical_trial_sae_template.md",{"path":949,"priority":300},"assets/consult_note_template.md",{"path":951,"priority":300},"assets/discharge_summary_template.md",{"path":953,"priority":300},"assets/hipaa_compliance_checklist.md",{"path":955,"priority":300},"assets/history_physical_template.md",{"path":957,"priority":300},"assets/lab_report_template.md",{"path":959,"priority":300},"assets/pathology_report_template.md",{"path":961,"priority":300},"assets/quality_checklist.md",{"path":963,"priority":300},"assets/radiology_report_template.md",{"path":965,"priority":300},"assets/soap_note_template.md",{"path":911,"priority":274},{"path":968,"priority":274},"references/case_report_guidelines.md",{"path":970,"priority":274},"references/clinical_trial_reporting.md",{"path":972,"priority":274},"references/data_presentation.md",{"path":974,"priority":274},"references/diagnostic_reports_standards.md",{"path":976,"priority":274},"references/medical_terminology.md",{"path":978,"priority":274},"references/patient_documentation.md",{"path":980,"priority":274},"references/peer_review_standards.md",{"path":982,"priority":274},"references/regulatory_compliance.md",{"path":984,"priority":300},"scripts/check_deidentification.py",{"path":986,"priority":300},"scripts/compliance_checker.py",{"path":988,"priority":300},"scripts/extract_clinical_data.py",{"path":990,"priority":300},"scripts/format_adverse_events.py",{"path":992,"priority":300},"scripts/generate_report_template.py",{"path":994,"priority":300},"scripts/terminology_validator.py",{"path":996,"priority":300},"scripts/validate_case_report.py",{"path":998,"priority":300},"scripts/validate_trial_report.py",{"basePath":1000,"description":1001,"displayName":1002,"installMethods":1003,"rationale":1004,"selectedPaths":1005,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-consciousness-council","Part of the AlterLab Academic Skills suite. Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like \"what would different experts think about this\", \"help me think through this from all sides\", \"council mode\", \"mind council\", or \"deliberate on this\". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.","alterlab-consciousness-council",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-consciousness-council/SKILL.md",[1006,1007],{"path":270,"priority":271},{"path":1008,"priority":274},"references/advanced-configurations.md",{"basePath":1010,"description":1011,"displayName":1012,"installMethods":1013,"rationale":1014,"selectedPaths":1015,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-dhdna-profiler","Part of the AlterLab Academic Skills suite. Extract cognitive patterns and thinking fingerprints from any text. Use this skill when the user wants to analyze how someone thinks, understand cognitive style, profile writing or speech patterns, compare thinking styles between people, asks \"what's my thinking style\", \"analyze how this person reasons\", \"cognitive profile\", \"thinking pattern\", \"DHDNA\", \"digital DNA\", or wants to understand the mind behind any text. Also trigger when the user provides text and wants deeper insight into the author's reasoning patterns, decision-making style, or cognitive signature.","alterlab-dhdna-profiler",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-dhdna-profiler/SKILL.md",[1016,1017],{"path":270,"priority":271},{"path":1018,"priority":274},"references/advanced-profiling.md",{"basePath":1020,"description":1021,"displayName":1022,"installMethods":1023,"rationale":1024,"selectedPaths":1025,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-iso13485","Part of the AlterLab Academic Skills suite. Comprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing documentation for medical device certification. Also use when users mention medical device regulations, QMS certification, FDA QMSR, EU MDR, or need help with quality system documentation.","alterlab-iso13485",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-iso13485/SKILL.md",[1026,1027,1029,1031,1033,1035,1037,1039,1041],{"path":270,"priority":271},{"path":1028,"priority":300},"assets/templates/procedures/CAPA-procedure-template.md",{"path":1030,"priority":300},"assets/templates/procedures/document-control-procedure-template.md",{"path":1032,"priority":300},"assets/templates/quality-manual-template.md",{"path":1034,"priority":274},"references/gap-analysis-checklist.md",{"path":1036,"priority":274},"references/iso-13485-requirements.md",{"path":1038,"priority":274},"references/mandatory-documents.md",{"path":1040,"priority":274},"references/quality-manual-guide.md",{"path":1042,"priority":300},"scripts/gap_analyzer.py",{"basePath":1044,"description":1045,"displayName":1046,"installMethods":1047,"rationale":1048,"selectedPaths":1049,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-neurokit2","Part of the AlterLab Academic Skills suite. Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.","alterlab-neurokit2",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-neurokit2/SKILL.md",[1050,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073],{"path":270,"priority":271},{"path":1052,"priority":274},"references/bio_module.md",{"path":1054,"priority":274},"references/complexity.md",{"path":1056,"priority":274},"references/ecg_cardiac.md",{"path":1058,"priority":274},"references/eda.md",{"path":1060,"priority":274},"references/eeg.md",{"path":1062,"priority":274},"references/emg.md",{"path":1064,"priority":274},"references/eog.md",{"path":1066,"priority":274},"references/epochs_events.md",{"path":1068,"priority":274},"references/hrv.md",{"path":1070,"priority":274},"references/ppg.md",{"path":1072,"priority":274},"references/rsp.md",{"path":602,"priority":274},{"basePath":1075,"description":1076,"displayName":1077,"installMethods":1078,"rationale":1079,"selectedPaths":1080,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-pydicom","Part of the AlterLab Academic Skills suite. Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.","alterlab-pydicom",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-pydicom/SKILL.md",[1081,1082,1084,1086,1088,1090],{"path":270,"priority":271},{"path":1083,"priority":274},"references/common_tags.md",{"path":1085,"priority":274},"references/transfer_syntaxes.md",{"path":1087,"priority":300},"scripts/anonymize_dicom.py",{"path":1089,"priority":300},"scripts/dicom_to_image.py",{"path":1091,"priority":300},"scripts/extract_metadata.py",{"basePath":1093,"description":1094,"displayName":1095,"installMethods":1096,"rationale":1097,"selectedPaths":1098,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-pyhealth","Part of the AlterLab Academic Skills suite. Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).","alterlab-pyhealth",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-pyhealth/SKILL.md",[1099,1100,1101,1103,1105,1106,1108],{"path":270,"priority":271},{"path":816,"priority":274},{"path":1102,"priority":274},"references/medical_coding.md",{"path":1104,"priority":274},"references/models.md",{"path":557,"priority":274},{"path":1107,"priority":274},"references/tasks.md",{"path":1109,"priority":274},"references/training_evaluation.md",{"basePath":1111,"description":1112,"displayName":1113,"installMethods":1114,"rationale":1115,"selectedPaths":1116,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-treatment-plans","Part of the AlterLab Academic Skills suite. Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.","alterlab-treatment-plans",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-treatment-plans/SKILL.md",[1117,1118,1120,1122,1124,1126,1128,1130,1132,1134,1135,1137,1138,1140,1142,1143,1145,1147,1149,1151,1153],{"path":270,"priority":271},{"path":1119,"priority":300},"assets/STYLING_QUICK_REFERENCE.md",{"path":1121,"priority":300},"assets/chronic_disease_management_plan.tex",{"path":1123,"priority":300},"assets/general_medical_treatment_plan.tex",{"path":1125,"priority":300},"assets/medical_treatment_plan.sty",{"path":1127,"priority":300},"assets/mental_health_treatment_plan.tex",{"path":1129,"priority":300},"assets/one_page_treatment_plan.tex",{"path":1131,"priority":300},"assets/pain_management_plan.tex",{"path":1133,"priority":300},"assets/perioperative_care_plan.tex",{"path":961,"priority":300},{"path":1136,"priority":300},"assets/rehabilitation_treatment_plan.tex",{"path":911,"priority":274},{"path":1139,"priority":274},"references/goal_setting_frameworks.md",{"path":1141,"priority":274},"references/intervention_guidelines.md",{"path":982,"priority":274},{"path":1144,"priority":274},"references/specialty_specific_guidelines.md",{"path":1146,"priority":274},"references/treatment_plan_standards.md",{"path":1148,"priority":300},"scripts/check_completeness.py",{"path":1150,"priority":300},"scripts/generate_template.py",{"path":1152,"priority":300},"scripts/timeline_generator.py",{"path":1154,"priority":300},"scripts/validate_treatment_plan.py",{"basePath":1156,"description":1157,"displayName":1158,"installMethods":1159,"rationale":1160,"selectedPaths":1161,"source":283,"sourceLanguage":18,"type":247},"skills/clinical-research/alterlab-what-if-oracle","Part of the AlterLab Academic Skills suite. Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case / worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","alterlab-what-if-oracle",{"claudeCode":12},"SKILL.md frontmatter at skills/clinical-research/alterlab-what-if-oracle/SKILL.md",[1162,1163],{"path":270,"priority":271},{"path":1164,"priority":274},"references/scenario-templates.md",{"basePath":1166,"description":1167,"displayName":1168,"installMethods":1169,"rationale":1170,"selectedPaths":1171,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-deep-research","Part of the AlterLab Academic Skills suite for faculty and researchers. Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.","alterlab-deep-research",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-deep-research/SKILL.md",[1172,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225],{"path":270,"priority":271},{"path":1174,"priority":300},"examples/exploratory_research.md",{"path":1176,"priority":300},"examples/fact_check_mode.md",{"path":1178,"priority":300},"examples/handoff_to_paper.md",{"path":1180,"priority":300},"examples/policy_analysis.md",{"path":1182,"priority":300},"examples/review_mode.md",{"path":1184,"priority":300},"examples/socratic_guided_research.md",{"path":1186,"priority":300},"examples/systematic_review.md",{"path":1188,"priority":274},"references/apa7_style_guide.md",{"path":1190,"priority":274},"references/equator_reporting_guidelines.md",{"path":1192,"priority":274},"references/ethics_checklist.md",{"path":1194,"priority":274},"references/failure_paths.md",{"path":1196,"priority":274},"references/interdisciplinary_bridges.md",{"path":1198,"priority":274},"references/irb_decision_tree.md",{"path":1200,"priority":274},"references/literature_monitoring_strategies.md",{"path":1202,"priority":274},"references/logical_fallacies.md",{"path":1204,"priority":274},"references/methodology_patterns.md",{"path":1206,"priority":274},"references/mode_selection_guide.md",{"path":1208,"priority":274},"references/preregistration_guide.md",{"path":1210,"priority":274},"references/socratic_questioning_framework.md",{"path":1212,"priority":274},"references/source_quality_hierarchy.md",{"path":1214,"priority":274},"references/systematic_review_toolkit.md",{"path":1216,"priority":300},"templates/evidence_assessment_template.md",{"path":1218,"priority":300},"templates/literature_matrix_template.md",{"path":1220,"priority":300},"templates/preregistration_template.md",{"path":1222,"priority":300},"templates/prisma_protocol_template.md",{"path":1224,"priority":300},"templates/prisma_report_template.md",{"path":1226,"priority":300},"templates/research_brief_template.md",{"basePath":1228,"description":1229,"displayName":1230,"installMethods":1231,"rationale":1232,"selectedPaths":1233,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-link-health","Part of the AlterLab Academic Skills suite. Meta-skill for auditing and repairing Markdown link health across a skills repo. Runs a four-tier pipeline (config hardening, intra-repo file-ref fixes, external URL substitutions, residual exclusions) and enforces a Tier 3 substitution guardrail that prevents regressions of previously-passing links. Designed for lychee-based GitHub Actions link checkers, but the methodology generalizes to markdown-link-check and similar tools. Triggers on: link audit, dead links, link health, lychee, broken links, link checker, markdown link audit, link-health audit, 404 audit, check-links failing, CI link-check, 連結健檢, 死鏈, 失效連結, 斷鏈檢查.","alterlab-link-health",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-link-health/SKILL.md",[1234,1235,1237,1239,1241,1243,1245],{"path":270,"priority":271},{"path":1236,"priority":300},"examples/pr-1-retrospective.md",{"path":1238,"priority":274},"references/known-debt-template.md",{"path":1240,"priority":274},"references/tier1-config.md",{"path":1242,"priority":274},"references/tier2-intra-repo.md",{"path":1244,"priority":274},"references/tier3-substitution.md",{"path":1246,"priority":274},"references/tier4-exclusions.md",{"basePath":1248,"description":1249,"displayName":1250,"installMethods":1251,"rationale":1252,"selectedPaths":1253,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-paper-reviewer","Part of the AlterLab Academic Skills suite for faculty and researchers. Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, and Socratic guided modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review.","alterlab-paper-reviewer",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-paper-reviewer/SKILL.md",[1254,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273],{"path":270,"priority":271},{"path":1256,"priority":300},"examples/hei_paper_review_example.md",{"path":1258,"priority":300},"examples/interdisciplinary_review_example.md",{"path":1260,"priority":274},"references/editorial_decision_standards.md",{"path":1262,"priority":274},"references/quality_rubrics.md",{"path":1264,"priority":274},"references/review_criteria_framework.md",{"path":1266,"priority":274},"references/statistical_reporting_standards.md",{"path":1268,"priority":274},"references/top_journals_by_field.md",{"path":1270,"priority":300},"templates/editorial_decision_template.md",{"path":1272,"priority":300},"templates/peer_review_report_template.md",{"path":1274,"priority":300},"templates/revision_response_template.md",{"basePath":1276,"description":1277,"displayName":1278,"installMethods":1279,"rationale":1280,"selectedPaths":1281,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-paper-writer","Part of the AlterLab Academic Skills suite for faculty and researchers. Academic paper writing skill with 12-agent pipeline. v2.4: LaTeX output formatting hardening — mandatory apa7 class, text justification fix, table column width formula, bilingual abstract centering, standardized font stack, PDF must compile from LaTeX. Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.","alterlab-paper-writer",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-paper-writer/SKILL.md",[1282,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1308,1310,1312,1314,1316,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341],{"path":270,"priority":271},{"path":1284,"priority":300},"examples/chinese_paper_example.md",{"path":1286,"priority":300},"examples/imrad_hei_example.md",{"path":1288,"priority":300},"examples/literature_review_example.md",{"path":1290,"priority":300},"examples/plan_mode_guided_writing.md",{"path":1292,"priority":300},"examples/revision_mode_example.md",{"path":1294,"priority":300},"examples/revision_recovery_example.md",{"path":1296,"priority":274},"references/abstract_writing_guide.md",{"path":1298,"priority":274},"references/academic_writing_style.md",{"path":1300,"priority":274},"references/apa7_chinese_citation_guide.md",{"path":1302,"priority":274},"references/apa7_extended_guide.md",{"path":1304,"priority":274},"references/citation_format_switcher.md",{"path":1306,"priority":274},"references/credit_authorship_guide.md",{"path":1194,"priority":274},{"path":1309,"priority":274},"references/funding_statement_guide.md",{"path":1311,"priority":274},"references/hei_domain_glossary.md",{"path":1313,"priority":274},"references/journal_submission_guide.md",{"path":1315,"priority":274},"references/latex_template_reference.md",{"path":1206,"priority":274},{"path":1318,"priority":274},"references/paper_structure_patterns.md",{"path":1320,"priority":274},"references/statistical_visualization_standards.md",{"path":1322,"priority":300},"templates/bilingual_abstract_template.md",{"path":1324,"priority":300},"templates/case_study_template.md",{"path":1326,"priority":300},"templates/conference_paper_template.md",{"path":1328,"priority":300},"templates/credit_statement_template.md",{"path":1330,"priority":300},"templates/funding_statement_template.md",{"path":1332,"priority":300},"templates/imrad_template.md",{"path":1334,"priority":300},"templates/latex_article_template.tex",{"path":1336,"priority":300},"templates/literature_review_template.md",{"path":1338,"priority":300},"templates/policy_brief_template.md",{"path":1340,"priority":300},"templates/revision_tracking_template.md",{"path":1342,"priority":300},"templates/theoretical_paper_template.md",{"basePath":1344,"description":1345,"displayName":1346,"installMethods":1347,"rationale":1348,"selectedPaths":1349,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-research-pipeline","Part of the AlterLab Academic Skills suite for faculty and researchers. Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates alterlab-deep-research, alterlab-paper-writer, and alterlab-paper-reviewer into a seamless 9-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.","alterlab-research-pipeline",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-research-pipeline/SKILL.md",[1350,1351,1353,1355,1357,1359,1361,1363,1365,1367],{"path":270,"priority":271},{"path":1352,"priority":300},"examples/full_pipeline_example.md",{"path":1354,"priority":300},"examples/integrity_failure_recovery.md",{"path":1356,"priority":300},"examples/mid_entry_example.md",{"path":1358,"priority":274},"references/claim_verification_protocol.md",{"path":1360,"priority":274},"references/mode_advisor.md",{"path":1362,"priority":274},"references/pipeline_state_machine.md",{"path":1364,"priority":274},"references/plagiarism_detection_protocol.md",{"path":1366,"priority":274},"references/team_collaboration_protocol.md",{"path":1368,"priority":300},"templates/pipeline_status_template.md",{"basePath":1370,"description":1371,"displayName":1372,"installMethods":1373,"rationale":1374,"selectedPaths":1375,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-teaching-design","Part of the AlterLab Academic Skills suite for faculty and researchers. Comprehensive course and teaching design assistant. Supports backward design (Wiggins & McTighe), constructive alignment (Biggs), Bloom's taxonomy alignment, rubric generation, assessment design (formative/summative), syllabus drafting, lesson planning, inclusive pedagogy, and online/hybrid course architecture. Triggers on: course design, syllabus, learning outcomes, rubric, assessment design, lesson plan, backward design, constructive alignment, Bloom's taxonomy, curriculum mapping, course redesign, inclusive pedagogy, hybrid course, online course design.","alterlab-teaching-design",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-teaching-design/SKILL.md",[1376,1377],{"path":270,"priority":271},{"path":1378,"priority":274},"references/teaching-frameworks.md",{"basePath":1380,"description":1381,"displayName":1382,"installMethods":1383,"rationale":1384,"selectedPaths":1385,"source":283,"sourceLanguage":18,"type":247},"skills/core/alterlab-thesis-supervisor","Part of the AlterLab Academic Skills suite for faculty and researchers. Comprehensive thesis and dissertation supervision assistant. Supports dissertation structure guidance (proposal through defense), chapter-by-chapter writing support (introduction, literature review, methodology, results, discussion), supervision strategies, committee management, defense preparation, timeline planning, feedback integration, formatting requirements (APA 7, Chicago, university styles), viva voce preparation, and examiner expectations. Triggers on: thesis, dissertation, supervision, defense preparation, viva, proposal defense, thesis structure, thesis chapter, literature review chapter, methodology chapter, results chapter, discussion chapter, thesis timeline, committee, thesis formatting, dissertation proposal.","alterlab-thesis-supervisor",{"claudeCode":12},"SKILL.md frontmatter at skills/core/alterlab-thesis-supervisor/SKILL.md",[1386,1387],{"path":270,"priority":271},{"path":1388,"priority":274},"references/thesis-guidelines.md",{"basePath":1390,"description":1391,"displayName":1392,"installMethods":1393,"rationale":1394,"selectedPaths":1395,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-dask","Part of the AlterLab Academic Skills suite. Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.","alterlab-dask",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-dask/SKILL.md",[1396,1397,1399,1401,1403,1405,1407],{"path":270,"priority":271},{"path":1398,"priority":274},"references/arrays.md",{"path":1400,"priority":274},"references/bags.md",{"path":1402,"priority":274},"references/best-practices.md",{"path":1404,"priority":274},"references/dataframes.md",{"path":1406,"priority":274},"references/futures.md",{"path":1408,"priority":274},"references/schedulers.md",{"basePath":1410,"description":1411,"displayName":1412,"installMethods":1413,"rationale":1414,"selectedPaths":1415,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-eda","Part of the AlterLab Academic Skills suite. Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.","alterlab-eda",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-eda/SKILL.md",[1416,1417,1419,1421,1423,1425,1427,1429,1431],{"path":270,"priority":271},{"path":1418,"priority":300},"assets/report_template.md",{"path":1420,"priority":274},"references/bioinformatics_genomics_formats.md",{"path":1422,"priority":274},"references/chemistry_molecular_formats.md",{"path":1424,"priority":274},"references/general_scientific_formats.md",{"path":1426,"priority":274},"references/microscopy_imaging_formats.md",{"path":1428,"priority":274},"references/proteomics_metabolomics_formats.md",{"path":1430,"priority":274},"references/spectroscopy_analytical_formats.md",{"path":1432,"priority":300},"scripts/eda_analyzer.py",{"basePath":1434,"description":1435,"displayName":1436,"installMethods":1437,"rationale":1438,"selectedPaths":1439,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-networkx","Part of the AlterLab Academic Skills suite. Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.","alterlab-networkx",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-networkx/SKILL.md",[1440,1441,1442,1444,1446,1448],{"path":270,"priority":271},{"path":293,"priority":274},{"path":1443,"priority":274},"references/generators.md",{"path":1445,"priority":274},"references/graph-basics.md",{"path":1447,"priority":274},"references/io.md",{"path":416,"priority":274},{"basePath":1450,"description":1451,"displayName":1452,"installMethods":1453,"rationale":1454,"selectedPaths":1455,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-polars","Part of the AlterLab Academic Skills suite. Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.","alterlab-polars",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-polars/SKILL.md",[1456,1457,1458,1459,1461,1463,1465],{"path":270,"priority":271},{"path":273,"priority":274},{"path":874,"priority":274},{"path":1460,"priority":274},"references/io_guide.md",{"path":1462,"priority":274},"references/operations.md",{"path":1464,"priority":274},"references/pandas_migration.md",{"path":1466,"priority":274},"references/transformations.md",{"basePath":1468,"description":1469,"displayName":1470,"installMethods":1471,"rationale":1472,"selectedPaths":1473,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-pufferlib","Part of the AlterLab Academic Skills suite. High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.","alterlab-pufferlib",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-pufferlib/SKILL.md",[1474,1475,1477,1479,1481,1483,1485,1487],{"path":270,"priority":271},{"path":1476,"priority":274},"references/environments.md",{"path":1478,"priority":274},"references/integration.md",{"path":1480,"priority":274},"references/policies.md",{"path":1482,"priority":274},"references/training.md",{"path":1484,"priority":274},"references/vectorization.md",{"path":1486,"priority":300},"scripts/env_template.py",{"path":1488,"priority":300},"scripts/train_template.py",{"basePath":1490,"description":1491,"displayName":1492,"installMethods":1493,"rationale":1494,"selectedPaths":1495,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-pymc","Part of the AlterLab Academic Skills suite. Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.","alterlab-pymc",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-pymc/SKILL.md",[1496,1497,1499,1501,1503,1505,1506,1508],{"path":270,"priority":271},{"path":1498,"priority":300},"assets/hierarchical_model_template.py",{"path":1500,"priority":300},"assets/linear_regression_template.py",{"path":1502,"priority":274},"references/distributions.md",{"path":1504,"priority":274},"references/sampling_inference.md",{"path":368,"priority":274},{"path":1507,"priority":300},"scripts/model_comparison.py",{"path":1509,"priority":300},"scripts/model_diagnostics.py",{"basePath":1511,"description":1512,"displayName":1513,"installMethods":1514,"rationale":1515,"selectedPaths":1516,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-pymoo","Part of the AlterLab Academic Skills suite. Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.","alterlab-pymoo",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-pymoo/SKILL.md",[1517,1518,1519,1521,1523,1525,1526,1528,1530,1532,1534],{"path":270,"priority":271},{"path":293,"priority":274},{"path":1520,"priority":274},"references/constraints_mcdm.md",{"path":1522,"priority":274},"references/operators.md",{"path":1524,"priority":274},"references/problems.md",{"path":416,"priority":274},{"path":1527,"priority":300},"scripts/custom_problem_example.py",{"path":1529,"priority":300},"scripts/decision_making_example.py",{"path":1531,"priority":300},"scripts/many_objective_example.py",{"path":1533,"priority":300},"scripts/multi_objective_example.py",{"path":1535,"priority":300},"scripts/single_objective_example.py",{"basePath":1537,"description":1538,"displayName":1539,"installMethods":1540,"rationale":1541,"selectedPaths":1542,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-pytorch-lightning","Part of the AlterLab Academic Skills suite. Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.","alterlab-pytorch-lightning",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-pytorch-lightning/SKILL.md",[1543,1544,1545,1547,1549,1551,1553,1555,1557,1559,1561],{"path":270,"priority":271},{"path":273,"priority":274},{"path":1546,"priority":274},"references/callbacks.md",{"path":1548,"priority":274},"references/data_module.md",{"path":1550,"priority":274},"references/distributed_training.md",{"path":1552,"priority":274},"references/lightning_module.md",{"path":1554,"priority":274},"references/logging.md",{"path":1556,"priority":274},"references/trainer.md",{"path":1558,"priority":300},"scripts/quick_trainer_setup.py",{"path":1560,"priority":300},"scripts/template_datamodule.py",{"path":1562,"priority":300},"scripts/template_lightning_module.py",{"basePath":1564,"description":1565,"displayName":1566,"installMethods":1567,"rationale":1568,"selectedPaths":1569,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-scikit-learn","Part of the AlterLab Academic Skills suite. Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.","alterlab-scikit-learn",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-scikit-learn/SKILL.md",[1570,1571,1573,1575,1576,1578,1580,1582,1584],{"path":270,"priority":271},{"path":1572,"priority":274},"references/model_evaluation.md",{"path":1574,"priority":274},"references/pipelines_and_composition.md",{"path":557,"priority":274},{"path":1577,"priority":274},"references/quick_reference.md",{"path":1579,"priority":274},"references/supervised_learning.md",{"path":1581,"priority":274},"references/unsupervised_learning.md",{"path":1583,"priority":300},"scripts/classification_pipeline.py",{"path":1585,"priority":300},"scripts/clustering_analysis.py",{"basePath":1587,"description":1588,"displayName":1589,"installMethods":1590,"rationale":1591,"selectedPaths":1592,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-scikit-survival","Part of the AlterLab Academic Skills suite. Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.","alterlab-scikit-survival",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-scikit-survival/SKILL.md",[1593,1594,1596,1598,1600,1602,1604],{"path":270,"priority":271},{"path":1595,"priority":274},"references/competing-risks.md",{"path":1597,"priority":274},"references/cox-models.md",{"path":1599,"priority":274},"references/data-handling.md",{"path":1601,"priority":274},"references/ensemble-models.md",{"path":1603,"priority":274},"references/evaluation-metrics.md",{"path":1605,"priority":274},"references/svm-models.md",{"basePath":1607,"description":1608,"displayName":1609,"installMethods":1610,"rationale":1611,"selectedPaths":1612,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-shap","Part of the AlterLab Academic Skills suite. Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.","alterlab-shap",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-shap/SKILL.md",[1613,1614,1616,1618,1620],{"path":270,"priority":271},{"path":1615,"priority":274},"references/explainers.md",{"path":1617,"priority":274},"references/plots.md",{"path":1619,"priority":274},"references/theory.md",{"path":368,"priority":274},{"basePath":1622,"description":1623,"displayName":1624,"installMethods":1625,"rationale":1626,"selectedPaths":1627,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-simpy","Part of the AlterLab Academic Skills suite. Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.","alterlab-simpy",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-simpy/SKILL.md",[1628,1629,1631,1633,1635,1637,1639,1641],{"path":270,"priority":271},{"path":1630,"priority":274},"references/events.md",{"path":1632,"priority":274},"references/monitoring.md",{"path":1634,"priority":274},"references/process-interaction.md",{"path":1636,"priority":274},"references/real-time.md",{"path":1638,"priority":274},"references/resources.md",{"path":1640,"priority":300},"scripts/basic_simulation_template.py",{"path":1642,"priority":300},"scripts/resource_monitor.py",{"basePath":1644,"description":1645,"displayName":1646,"installMethods":1647,"rationale":1648,"selectedPaths":1649,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-stable-baselines3","Part of the AlterLab Academic Skills suite. Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.","alterlab-stable-baselines3",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-stable-baselines3/SKILL.md",[1650,1651,1652,1653,1655,1657,1659,1661],{"path":270,"priority":271},{"path":293,"priority":274},{"path":1546,"priority":274},{"path":1654,"priority":274},"references/custom_environments.md",{"path":1656,"priority":274},"references/vectorized_envs.md",{"path":1658,"priority":300},"scripts/custom_env_template.py",{"path":1660,"priority":300},"scripts/evaluate_agent.py",{"path":1662,"priority":300},"scripts/train_rl_agent.py",{"basePath":1664,"description":1665,"displayName":1666,"installMethods":1667,"rationale":1668,"selectedPaths":1669,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-statistical-analysis","Part of the AlterLab Academic Skills suite. Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","alterlab-statistical-analysis",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-statistical-analysis/SKILL.md",[1670,1671,1673,1675,1677,1679,1681],{"path":270,"priority":271},{"path":1672,"priority":274},"references/assumptions_and_diagnostics.md",{"path":1674,"priority":274},"references/bayesian_statistics.md",{"path":1676,"priority":274},"references/effect_sizes_and_power.md",{"path":1678,"priority":274},"references/reporting_standards.md",{"path":1680,"priority":274},"references/test_selection_guide.md",{"path":1682,"priority":300},"scripts/assumption_checks.py",{"basePath":1684,"description":1685,"displayName":1686,"installMethods":1687,"rationale":1688,"selectedPaths":1689,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-statsmodels","Part of the AlterLab Academic Skills suite. Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.","alterlab-statsmodels",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-statsmodels/SKILL.md",[1690,1691,1693,1695,1697,1699],{"path":270,"priority":271},{"path":1692,"priority":274},"references/discrete_choice.md",{"path":1694,"priority":274},"references/glm.md",{"path":1696,"priority":274},"references/linear_models.md",{"path":1698,"priority":274},"references/stats_diagnostics.md",{"path":1700,"priority":274},"references/time_series.md",{"basePath":1702,"description":1703,"displayName":1704,"installMethods":1705,"rationale":1706,"selectedPaths":1707,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-sympy","Part of the AlterLab Academic Skills suite. Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.","alterlab-sympy",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-sympy/SKILL.md",[1708,1709,1711,1713,1715,1717],{"path":270,"priority":271},{"path":1710,"priority":274},"references/advanced-topics.md",{"path":1712,"priority":274},"references/code-generation-printing.md",{"path":1714,"priority":274},"references/core-capabilities.md",{"path":1716,"priority":274},"references/matrices-linear-algebra.md",{"path":1718,"priority":274},"references/physics-mechanics.md",{"basePath":243,"description":10,"displayName":246,"installMethods":1720,"rationale":1721,"selectedPaths":1722,"source":283,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-timesfm/SKILL.md",[1723,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1767,1769,1771,1773],{"path":270,"priority":271},{"path":1725,"priority":300},"examples/anomaly-detection/detect_anomalies.py",{"path":1727,"priority":300},"examples/anomaly-detection/output/anomaly_detection.json",{"path":1729,"priority":300},"examples/anomaly-detection/output/anomaly_detection.png",{"path":1731,"priority":300},"examples/covariates-forecasting/demo_covariates.py",{"path":1733,"priority":300},"examples/covariates-forecasting/output/covariates_data.png",{"path":1735,"priority":300},"examples/covariates-forecasting/output/covariates_metadata.json",{"path":1737,"priority":300},"examples/covariates-forecasting/output/sales_with_covariates.csv",{"path":1739,"priority":300},"examples/global-temperature/README.md",{"path":1741,"priority":300},"examples/global-temperature/generate_animation_data.py",{"path":1743,"priority":300},"examples/global-temperature/generate_gif.py",{"path":1745,"priority":300},"examples/global-temperature/generate_html.py",{"path":1747,"priority":300},"examples/global-temperature/output/animation_data.json",{"path":1749,"priority":300},"examples/global-temperature/output/forecast_animation.gif",{"path":1751,"priority":300},"examples/global-temperature/output/forecast_output.csv",{"path":1753,"priority":300},"examples/global-temperature/output/forecast_output.json",{"path":1755,"priority":300},"examples/global-temperature/output/forecast_visualization.png",{"path":1757,"priority":300},"examples/global-temperature/output/interactive_forecast.html",{"path":1759,"priority":300},"examples/global-temperature/run_example.sh",{"path":1761,"priority":300},"examples/global-temperature/run_forecast.py",{"path":1763,"priority":300},"examples/global-temperature/temperature_anomaly.csv",{"path":1765,"priority":300},"examples/global-temperature/visualize_forecast.py",{"path":414,"priority":274},{"path":1768,"priority":274},"references/data_preparation.md",{"path":1770,"priority":274},"references/system_requirements.md",{"path":1772,"priority":300},"scripts/check_system.py",{"path":1774,"priority":300},"scripts/forecast_csv.py",{"basePath":1776,"description":1777,"displayName":1778,"installMethods":1779,"rationale":1780,"selectedPaths":1781,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-torch-geometric","Part of the AlterLab Academic Skills suite. Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.","alterlab-torch-geometric",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-torch-geometric/SKILL.md",[1782,1783,1785,1787,1789,1791,1793],{"path":270,"priority":271},{"path":1784,"priority":274},"references/datasets_reference.md",{"path":1786,"priority":274},"references/layers_reference.md",{"path":1788,"priority":274},"references/transforms_reference.md",{"path":1790,"priority":300},"scripts/benchmark_model.py",{"path":1792,"priority":300},"scripts/create_gnn_template.py",{"path":1794,"priority":300},"scripts/visualize_graph.py",{"basePath":1796,"description":1797,"displayName":1798,"installMethods":1799,"rationale":1800,"selectedPaths":1801,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-transformers","Part of the AlterLab Academic Skills suite. This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.","alterlab-transformers",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-transformers/SKILL.md",[1802,1803,1805,1806,1808,1810],{"path":270,"priority":271},{"path":1804,"priority":274},"references/generation.md",{"path":1104,"priority":274},{"path":1807,"priority":274},"references/pipelines.md",{"path":1809,"priority":274},"references/tokenizers.md",{"path":1482,"priority":274},{"basePath":1812,"description":1813,"displayName":1814,"installMethods":1815,"rationale":1816,"selectedPaths":1817,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-umap","Part of the AlterLab Academic Skills suite. UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.","alterlab-umap",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-umap/SKILL.md",[1818,1819],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":1821,"description":1822,"displayName":1823,"installMethods":1824,"rationale":1825,"selectedPaths":1826,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-vaex","Part of the AlterLab Academic Skills suite. Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","alterlab-vaex",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-vaex/SKILL.md",[1827,1828,1830,1832,1833,1834,1836],{"path":270,"priority":271},{"path":1829,"priority":274},"references/core_dataframes.md",{"path":1831,"priority":274},"references/data_processing.md",{"path":280,"priority":274},{"path":553,"priority":274},{"path":1835,"priority":274},"references/performance.md",{"path":416,"priority":274},{"basePath":1838,"description":1839,"displayName":1840,"installMethods":1841,"rationale":1842,"selectedPaths":1843,"source":283,"sourceLanguage":18,"type":247},"skills/data-science/alterlab-zarr","Part of the AlterLab Academic Skills suite. Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.","alterlab-zarr",{"claudeCode":12},"SKILL.md frontmatter at skills/data-science/alterlab-zarr/SKILL.md",[1844,1845],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":1847,"description":1848,"displayName":1849,"installMethods":1850,"rationale":1851,"selectedPaths":1852,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-alphafold-db","Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology. Part of the AlterLab Academic Skills suite.","alterlab-alphafold-db",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-alphafold-db/SKILL.md",[1853,1854],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":1856,"description":1857,"displayName":1858,"installMethods":1859,"rationale":1860,"selectedPaths":1861,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-arxiv","Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories. Part of the AlterLab Academic Skills suite.","alterlab-arxiv",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-arxiv/SKILL.md",[1862,1863,1864],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1865,"priority":300},"scripts/arxiv_search.py",{"basePath":1867,"description":1868,"displayName":1869,"installMethods":1870,"rationale":1871,"selectedPaths":1872,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-bindingdb","Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies. Part of the AlterLab Academic Skills suite.","alterlab-bindingdb",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-bindingdb/SKILL.md",[1873,1874],{"path":270,"priority":271},{"path":1875,"priority":274},"references/affinity_queries.md",{"basePath":1877,"description":1878,"displayName":1879,"installMethods":1880,"rationale":1881,"selectedPaths":1882,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-biorxiv","Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews. Part of the AlterLab Academic Skills suite.","alterlab-biorxiv",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-biorxiv/SKILL.md",[1883,1884,1885],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1886,"priority":300},"scripts/biorxiv_search.py",{"basePath":1888,"description":1889,"displayName":1890,"installMethods":1891,"rationale":1892,"selectedPaths":1893,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-brenda","Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis. Part of the AlterLab Academic Skills suite.","alterlab-brenda",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-brenda/SKILL.md",[1894,1895,1896,1898,1900],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1897,"priority":300},"scripts/brenda_queries.py",{"path":1899,"priority":300},"scripts/brenda_visualization.py",{"path":1901,"priority":300},"scripts/enzyme_pathway_builder.py",{"basePath":1903,"description":1904,"displayName":1905,"installMethods":1906,"rationale":1907,"selectedPaths":1908,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-cbioportal","Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies. Essential for cancer target validation, oncogene/tumor suppressor analysis, and patient-level genomic profiling. Part of the AlterLab Academic Skills suite.","alterlab-cbioportal",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-cbioportal/SKILL.md",[1909,1910],{"path":270,"priority":271},{"path":1911,"priority":274},"references/study_exploration.md",{"basePath":1913,"description":1914,"displayName":1915,"installMethods":1916,"rationale":1917,"selectedPaths":1918,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-chembl","Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry. Part of the AlterLab Academic Skills suite.","alterlab-chembl",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-chembl/SKILL.md",[1919,1920,1921],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1922,"priority":300},"scripts/example_queries.py",{"basePath":1924,"description":1925,"displayName":1926,"installMethods":1927,"rationale":1928,"selectedPaths":1929,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-clinicaltrials","Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching. Part of the AlterLab Academic Skills suite.","alterlab-clinicaltrials",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-clinicaltrials/SKILL.md",[1930,1931,1932],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1933,"priority":300},"scripts/query_clinicaltrials.py",{"basePath":1935,"description":1936,"displayName":1937,"installMethods":1938,"rationale":1939,"selectedPaths":1940,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-clinpgx","Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions. Part of the AlterLab Academic Skills suite.","alterlab-clinpgx",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-clinpgx/SKILL.md",[1941,1942,1943],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1944,"priority":300},"scripts/query_clinpgx.py",{"basePath":1946,"description":1947,"displayName":1948,"installMethods":1949,"rationale":1950,"selectedPaths":1951,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-clinvar","Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine. Part of the AlterLab Academic Skills suite.","alterlab-clinvar",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-clinvar/SKILL.md",[1952,1953,1954,1956],{"path":270,"priority":271},{"path":414,"priority":274},{"path":1955,"priority":274},"references/clinical_significance.md",{"path":1957,"priority":274},"references/data_formats.md",{"basePath":1959,"description":1960,"displayName":1961,"installMethods":1962,"rationale":1963,"selectedPaths":1964,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-cosmic","Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication. Part of the AlterLab Academic Skills suite.","alterlab-cosmic",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-cosmic/SKILL.md",[1965,1966,1968],{"path":270,"priority":271},{"path":1967,"priority":274},"references/cosmic_data_reference.md",{"path":1969,"priority":300},"scripts/download_cosmic.py",{"basePath":1971,"description":1972,"displayName":1973,"installMethods":1974,"rationale":1975,"selectedPaths":1976,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-datacommons","Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.","alterlab-datacommons",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-datacommons/SKILL.md",[1977,1978,1980,1982,1984],{"path":270,"priority":271},{"path":1979,"priority":274},"references/getting_started.md",{"path":1981,"priority":274},"references/node.md",{"path":1983,"priority":274},"references/observation.md",{"path":1985,"priority":274},"references/resolve.md",{"basePath":1987,"description":1988,"displayName":1989,"installMethods":1990,"rationale":1991,"selectedPaths":1992,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-depmap","Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets. Part of the AlterLab Academic Skills suite.","alterlab-depmap",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-depmap/SKILL.md",[1993,1994],{"path":270,"priority":271},{"path":1995,"priority":274},"references/dependency_analysis.md",{"basePath":1997,"description":1998,"displayName":1999,"installMethods":2000,"rationale":2001,"selectedPaths":2002,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-drugbank","Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank. Part of the AlterLab Academic Skills suite.","alterlab-drugbank",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-drugbank/SKILL.md",[2003,2004,2006,2008,2010,2012,2014],{"path":270,"priority":271},{"path":2005,"priority":274},"references/chemical-analysis.md",{"path":2007,"priority":274},"references/data-access.md",{"path":2009,"priority":274},"references/drug-queries.md",{"path":2011,"priority":274},"references/interactions.md",{"path":2013,"priority":274},"references/targets-pathways.md",{"path":2015,"priority":300},"scripts/drugbank_helper.py",{"basePath":2017,"description":2018,"displayName":2019,"installMethods":2020,"rationale":2021,"selectedPaths":2022,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-ena","Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats. Part of the AlterLab Academic Skills suite.","alterlab-ena",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-ena/SKILL.md",[2023,2024],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2026,"description":2027,"displayName":2028,"installMethods":2029,"rationale":2030,"selectedPaths":2031,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-ensembl","Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research. Part of the AlterLab Academic Skills suite.","alterlab-ensembl",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-ensembl/SKILL.md",[2032,2033,2035],{"path":270,"priority":271},{"path":2034,"priority":274},"references/api_endpoints.md",{"path":2036,"priority":300},"scripts/ensembl_query.py",{"basePath":2038,"description":2039,"displayName":2040,"installMethods":2041,"rationale":2042,"selectedPaths":2043,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-fda","Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research. Part of the AlterLab Academic Skills suite.","alterlab-fda",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-fda/SKILL.md",[2044,2045,2047,2049,2051,2053,2055,2057,2059],{"path":270,"priority":271},{"path":2046,"priority":274},"references/animal_veterinary.md",{"path":2048,"priority":274},"references/api_basics.md",{"path":2050,"priority":274},"references/devices.md",{"path":2052,"priority":274},"references/drugs.md",{"path":2054,"priority":274},"references/foods.md",{"path":2056,"priority":274},"references/other.md",{"path":2058,"priority":300},"scripts/fda_examples.py",{"path":2060,"priority":300},"scripts/fda_query.py",{"basePath":2062,"description":2063,"displayName":2064,"installMethods":2065,"rationale":2066,"selectedPaths":2067,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-gene-db","Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis. Part of the AlterLab Academic Skills suite.","alterlab-gene-db",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-gene-db/SKILL.md",[2068,2069,2070,2071,2073,2075],{"path":270,"priority":271},{"path":414,"priority":274},{"path":614,"priority":274},{"path":2072,"priority":300},"scripts/batch_gene_lookup.py",{"path":2074,"priority":300},"scripts/fetch_gene_data.py",{"path":2076,"priority":300},"scripts/query_gene.py",{"basePath":2078,"description":2079,"displayName":2080,"installMethods":2081,"rationale":2082,"selectedPaths":2083,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-geo","Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis. Part of the AlterLab Academic Skills suite.","alterlab-geo",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-geo/SKILL.md",[2084,2085],{"path":270,"priority":271},{"path":2086,"priority":274},"references/geo_reference.md",{"basePath":2088,"description":2089,"displayName":2090,"installMethods":2091,"rationale":2092,"selectedPaths":2093,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-gnomad","Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.","alterlab-gnomad",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-gnomad/SKILL.md",[2094,2095,2097],{"path":270,"priority":271},{"path":2096,"priority":274},"references/graphql_queries.md",{"path":2098,"priority":274},"references/variant_interpretation.md",{"basePath":2100,"description":2101,"displayName":2102,"installMethods":2103,"rationale":2104,"selectedPaths":2105,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-gtex","Query GTEx (Genotype-Tissue Expression) portal for tissue-specific gene expression, eQTLs (expression quantitative trait loci), and sQTLs. Essential for linking GWAS variants to gene regulation, understanding tissue-specific expression, and interpreting non-coding variant effects. Part of the AlterLab Academic Skills suite.","alterlab-gtex",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-gtex/SKILL.md",[2106,2107],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2109,"description":2110,"displayName":2111,"installMethods":2112,"rationale":2113,"selectedPaths":2114,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-gwas","Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores. Part of the AlterLab Academic Skills suite.","alterlab-gwas",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-gwas/SKILL.md",[2115,2116],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2118,"description":2119,"displayName":2120,"installMethods":2121,"rationale":2122,"selectedPaths":2123,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-hmdb","Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification. Part of the AlterLab Academic Skills suite.","alterlab-hmdb",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-hmdb/SKILL.md",[2124,2125],{"path":270,"priority":271},{"path":2126,"priority":274},"references/hmdb_data_fields.md",{"basePath":2128,"description":2129,"displayName":2130,"installMethods":2131,"rationale":2132,"selectedPaths":2133,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-imaging-data-commons","Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses. Part of the AlterLab Academic Skills suite.","alterlab-imaging-data-commons",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-imaging-data-commons/SKILL.md",[2134,2135,2137,2139,2141,2143,2145,2147,2149,2151],{"path":270,"priority":271},{"path":2136,"priority":274},"references/bigquery_guide.md",{"path":2138,"priority":274},"references/cli_guide.md",{"path":2140,"priority":274},"references/clinical_data_guide.md",{"path":2142,"priority":274},"references/cloud_storage_guide.md",{"path":2144,"priority":274},"references/dicomweb_guide.md",{"path":2146,"priority":274},"references/digital_pathology_guide.md",{"path":2148,"priority":274},"references/index_tables_guide.md",{"path":2150,"priority":274},"references/sql_patterns.md",{"path":2152,"priority":274},"references/use_cases.md",{"basePath":2154,"description":2155,"displayName":2156,"installMethods":2157,"rationale":2158,"selectedPaths":2159,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-interpro","Query InterPro for protein family, domain, and functional site annotations. Integrates Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, and 11 other member databases. Use for protein function prediction, domain architecture analysis, evolutionary classification, and GO term mapping. Part of the AlterLab Academic Skills suite.","alterlab-interpro",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-interpro/SKILL.md",[2160,2161],{"path":270,"priority":271},{"path":2162,"priority":274},"references/domain_analysis.md",{"basePath":2164,"description":2165,"displayName":2166,"installMethods":2167,"rationale":2168,"selectedPaths":2169,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-jaspar","Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation. Part of the AlterLab Academic Skills suite.","alterlab-jaspar",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-jaspar/SKILL.md",[2170,2171],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2173,"description":2174,"displayName":2175,"installMethods":2176,"rationale":2177,"selectedPaths":2178,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-kegg","Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control. Part of the AlterLab Academic Skills suite.","alterlab-kegg",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-kegg/SKILL.md",[2179,2180,2182],{"path":270,"priority":271},{"path":2181,"priority":274},"references/kegg_reference.md",{"path":2183,"priority":300},"scripts/kegg_api.py",{"basePath":2185,"description":2186,"displayName":2187,"installMethods":2188,"rationale":2189,"selectedPaths":2190,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-metabolomics-wb","Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery. Part of the AlterLab Academic Skills suite.","alterlab-metabolomics-wb",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-metabolomics-wb/SKILL.md",[2191,2192],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2194,"description":2195,"displayName":2196,"installMethods":2197,"rationale":2198,"selectedPaths":2199,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-monarch","Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species. Integrates OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use for rare disease gene discovery, phenotype-to-gene mapping, cross-species disease modeling, and HPO term lookup. Part of the AlterLab Academic Skills suite.","alterlab-monarch",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-monarch/SKILL.md",[2200,2201],{"path":270,"priority":271},{"path":2202,"priority":274},"references/phenotype_ontology.md",{"basePath":2204,"description":2205,"displayName":2206,"installMethods":2207,"rationale":2208,"selectedPaths":2209,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-openalex","Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries. Part of the AlterLab Academic Skills suite.","alterlab-openalex",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-openalex/SKILL.md",[2210,2211,2212,2214,2216],{"path":270,"priority":271},{"path":769,"priority":274},{"path":2213,"priority":274},"references/common_queries.md",{"path":2215,"priority":300},"scripts/openalex_client.py",{"path":2217,"priority":300},"scripts/query_helpers.py",{"basePath":2219,"description":2220,"displayName":2221,"installMethods":2222,"rationale":2223,"selectedPaths":2224,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-opentargets","Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification. Part of the AlterLab Academic Skills suite.","alterlab-opentargets",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-opentargets/SKILL.md",[2225,2226,2227,2229,2231],{"path":270,"priority":271},{"path":414,"priority":274},{"path":2228,"priority":274},"references/evidence_types.md",{"path":2230,"priority":274},"references/target_annotations.md",{"path":2232,"priority":300},"scripts/query_opentargets.py",{"basePath":2234,"description":2235,"displayName":2236,"installMethods":2237,"rationale":2238,"selectedPaths":2239,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-pdb","Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery. Part of the AlterLab Academic Skills suite.","alterlab-pdb",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-pdb/SKILL.md",[2240,2241],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2243,"description":2244,"displayName":2245,"installMethods":2246,"rationale":2247,"selectedPaths":2248,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-pubchem","Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics. Part of the AlterLab Academic Skills suite.","alterlab-pubchem",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-pubchem/SKILL.md",[2249,2250,2251,2253],{"path":270,"priority":271},{"path":414,"priority":274},{"path":2252,"priority":300},"scripts/bioactivity_query.py",{"path":2254,"priority":300},"scripts/compound_search.py",{"basePath":2256,"description":2257,"displayName":2258,"installMethods":2259,"rationale":2260,"selectedPaths":2261,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-pubmed","Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations. Part of the AlterLab Academic Skills suite.","alterlab-pubmed",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-pubmed/SKILL.md",[2262,2263,2264,2265],{"path":270,"priority":271},{"path":414,"priority":274},{"path":2213,"priority":274},{"path":2266,"priority":274},"references/search_syntax.md",{"basePath":2268,"description":2269,"displayName":2270,"installMethods":2271,"rationale":2272,"selectedPaths":2273,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-reactome","Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies. Part of the AlterLab Academic Skills suite.","alterlab-reactome",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-reactome/SKILL.md",[2274,2275,2276],{"path":270,"priority":271},{"path":414,"priority":274},{"path":2277,"priority":300},"scripts/reactome_query.py",{"basePath":2279,"description":2280,"displayName":2281,"installMethods":2282,"rationale":2283,"selectedPaths":2284,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-string-db","Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology. Part of the AlterLab Academic Skills suite.","alterlab-string-db",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-string-db/SKILL.md",[2285,2286,2288],{"path":270,"priority":271},{"path":2287,"priority":274},"references/string_reference.md",{"path":2289,"priority":300},"scripts/string_api.py",{"basePath":2291,"description":2292,"displayName":2293,"installMethods":2294,"rationale":2295,"selectedPaths":2296,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-uniprot","Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control. Part of the AlterLab Academic Skills suite.","alterlab-uniprot",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-uniprot/SKILL.md",[2297,2298,2300,2302,2304,2306],{"path":270,"priority":271},{"path":2299,"priority":274},"references/api_examples.md",{"path":2301,"priority":274},"references/api_fields.md",{"path":2303,"priority":274},"references/id_mapping_databases.md",{"path":2305,"priority":274},"references/query_syntax.md",{"path":2307,"priority":300},"scripts/uniprot_client.py",{"basePath":2309,"description":2310,"displayName":2311,"installMethods":2312,"rationale":2313,"selectedPaths":2314,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-uspto","Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches. Part of the AlterLab Academic Skills suite.","alterlab-uspto",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-uspto/SKILL.md",[2315,2316,2318,2320,2322,2324,2326,2328],{"path":270,"priority":271},{"path":2317,"priority":274},"references/additional_apis.md",{"path":2319,"priority":274},"references/patentsearch_api.md",{"path":2321,"priority":274},"references/peds_api.md",{"path":2323,"priority":274},"references/trademark_api.md",{"path":2325,"priority":300},"scripts/patent_search.py",{"path":2327,"priority":300},"scripts/peds_client.py",{"path":2329,"priority":300},"scripts/trademark_client.py",{"basePath":2331,"description":2332,"displayName":2333,"installMethods":2334,"rationale":2335,"selectedPaths":2336,"source":283,"sourceLanguage":18,"type":247},"skills/databases/alterlab-zinc-db","Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery. Part of the AlterLab Academic Skills suite.","alterlab-zinc-db",{"claudeCode":12},"SKILL.md frontmatter at skills/databases/alterlab-zinc-db/SKILL.md",[2337,2338],{"path":270,"priority":271},{"path":414,"priority":274},{"basePath":2340,"description":2341,"displayName":2342,"installMethods":2343,"rationale":2344,"selectedPaths":2345,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-docx","Part of the AlterLab Academic Skills suite. Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.","alterlab-docx",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-docx/SKILL.md",[2346,2347,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388],{"path":270,"priority":271},{"path":2348,"priority":2349},"LICENSE.txt","high",{"path":2351,"priority":300},"scripts/__init__.py",{"path":2353,"priority":300},"scripts/accept_changes.py",{"path":2355,"priority":300},"scripts/comment.py",{"path":2357,"priority":300},"scripts/office/helpers/__init__.py",{"path":2359,"priority":300},"scripts/office/helpers/merge_runs.py",{"path":2361,"priority":300},"scripts/office/helpers/simplify_redlines.py",{"path":2363,"priority":300},"scripts/office/pack.py",{"path":2365,"priority":300},"scripts/office/soffice.py",{"path":2367,"priority":300},"scripts/office/unpack.py",{"path":2369,"priority":300},"scripts/office/validate.py",{"path":2371,"priority":300},"scripts/office/validators/__init__.py",{"path":2373,"priority":300},"scripts/office/validators/base.py",{"path":2375,"priority":300},"scripts/office/validators/docx.py",{"path":2377,"priority":300},"scripts/office/validators/pptx.py",{"path":2379,"priority":300},"scripts/office/validators/redlining.py",{"path":2381,"priority":300},"scripts/templates/comments.xml",{"path":2383,"priority":300},"scripts/templates/commentsExtended.xml",{"path":2385,"priority":300},"scripts/templates/commentsExtensible.xml",{"path":2387,"priority":300},"scripts/templates/commentsIds.xml",{"path":2389,"priority":300},"scripts/templates/people.xml",{"basePath":2391,"description":2392,"displayName":2393,"installMethods":2394,"rationale":2395,"selectedPaths":2396,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-markitdown","Part of the AlterLab Academic Skills suite. Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.","alterlab-markitdown",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-markitdown/SKILL.md",[2397,2398,2400,2401,2403,2405,2407],{"path":270,"priority":271},{"path":2399,"priority":300},"assets/example_usage.md",{"path":414,"priority":274},{"path":2402,"priority":274},"references/file_formats.md",{"path":2404,"priority":300},"scripts/batch_convert.py",{"path":2406,"priority":300},"scripts/convert_literature.py",{"path":2408,"priority":300},"scripts/convert_with_ai.py",{"basePath":2410,"description":2411,"displayName":2412,"installMethods":2413,"rationale":2414,"selectedPaths":2415,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-open-notebook","Part of the AlterLab Academic Skills suite. Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.","alterlab-open-notebook",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-open-notebook/SKILL.md",[2416,2417,2418,2420,2422,2423,2425,2427,2429],{"path":270,"priority":271},{"path":414,"priority":274},{"path":2419,"priority":274},"references/architecture.md",{"path":2421,"priority":274},"references/configuration.md",{"path":796,"priority":274},{"path":2424,"priority":300},"scripts/chat_interaction.py",{"path":2426,"priority":300},"scripts/notebook_management.py",{"path":2428,"priority":300},"scripts/source_ingestion.py",{"path":2430,"priority":300},"scripts/test_open_notebook_skill.py",{"basePath":2432,"description":2433,"displayName":2434,"installMethods":2435,"rationale":2436,"selectedPaths":2437,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-pdf","Part of the AlterLab Academic Skills suite. Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.","alterlab-pdf",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-pdf/SKILL.md",[2438,2439,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458],{"path":270,"priority":271},{"path":2348,"priority":2349},{"path":2441,"priority":274},"forms.md",{"path":2443,"priority":274},"reference.md",{"path":2445,"priority":300},"scripts/check_bounding_boxes.py",{"path":2447,"priority":300},"scripts/check_fillable_fields.py",{"path":2449,"priority":300},"scripts/convert_pdf_to_images.py",{"path":2451,"priority":300},"scripts/create_validation_image.py",{"path":2453,"priority":300},"scripts/extract_form_field_info.py",{"path":2455,"priority":300},"scripts/extract_form_structure.py",{"path":2457,"priority":300},"scripts/fill_fillable_fields.py",{"path":2459,"priority":300},"scripts/fill_pdf_form_with_annotations.py",{"basePath":2461,"description":2462,"displayName":2463,"installMethods":2464,"rationale":2465,"selectedPaths":2466,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-pptx","Part of the AlterLab Academic Skills suite. Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.","alterlab-pptx",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-pptx/SKILL.md",[2467,2468,2469,2471,2473,2474,2476,2478,2479,2480,2481,2482,2483,2484,2485,2486,2487,2488,2489,2490],{"path":270,"priority":271},{"path":2348,"priority":2349},{"path":2470,"priority":274},"editing.md",{"path":2472,"priority":274},"pptxgenjs.md",{"path":2351,"priority":300},{"path":2475,"priority":300},"scripts/add_slide.py",{"path":2477,"priority":300},"scripts/clean.py",{"path":2357,"priority":300},{"path":2359,"priority":300},{"path":2361,"priority":300},{"path":2363,"priority":300},{"path":2365,"priority":300},{"path":2367,"priority":300},{"path":2369,"priority":300},{"path":2371,"priority":300},{"path":2373,"priority":300},{"path":2375,"priority":300},{"path":2377,"priority":300},{"path":2379,"priority":300},{"path":2491,"priority":300},"scripts/thumbnail.py",{"basePath":2493,"description":2494,"displayName":2495,"installMethods":2496,"rationale":2497,"selectedPaths":2498,"source":283,"sourceLanguage":18,"type":247},"skills/document-tools/alterlab-xlsx","Part of the AlterLab Academic Skills suite. Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.","alterlab-xlsx",{"claudeCode":12},"SKILL.md frontmatter at skills/document-tools/alterlab-xlsx/SKILL.md",[2499,2500,2501,2502,2503,2504,2505,2506,2507,2508,2509,2510,2511,2512,2513],{"path":270,"priority":271},{"path":2348,"priority":2349},{"path":2357,"priority":300},{"path":2359,"priority":300},{"path":2361,"priority":300},{"path":2363,"priority":300},{"path":2365,"priority":300},{"path":2367,"priority":300},{"path":2369,"priority":300},{"path":2371,"priority":300},{"path":2373,"priority":300},{"path":2375,"priority":300},{"path":2377,"priority":300},{"path":2379,"priority":300},{"path":2514,"priority":300},"scripts/recalc.py",{"basePath":2516,"description":2517,"displayName":2518,"installMethods":2519,"rationale":2520,"selectedPaths":2521,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-adaptyv","Part of the AlterLab Academic Skills suite. Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.","alterlab-adaptyv",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-adaptyv/SKILL.md",[2522],{"path":270,"priority":271},{"basePath":2524,"description":2525,"displayName":2526,"installMethods":2527,"rationale":2528,"selectedPaths":2529,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-aeon","Part of the AlterLab Academic Skills suite. This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.","alterlab-aeon",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-aeon/SKILL.md",[2530,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551],{"path":270,"priority":271},{"path":2532,"priority":274},"references/anomaly_detection.md",{"path":2534,"priority":274},"references/classification.md",{"path":2536,"priority":274},"references/clustering.md",{"path":2538,"priority":274},"references/datasets_benchmarking.md",{"path":2540,"priority":274},"references/distances.md",{"path":2542,"priority":274},"references/forecasting.md",{"path":2544,"priority":274},"references/networks.md",{"path":2546,"priority":274},"references/regression.md",{"path":2548,"priority":274},"references/segmentation.md",{"path":2550,"priority":274},"references/similarity_search.md",{"path":1466,"priority":274},{"basePath":2553,"description":2554,"displayName":2555,"installMethods":2556,"rationale":2557,"selectedPaths":2558,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-astropy","Part of the AlterLab Academic Skills suite. Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.","alterlab-astropy",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-astropy/SKILL.md",[2559,2560,2562,2564,2566,2568,2570,2572],{"path":270,"priority":271},{"path":2561,"priority":274},"references/coordinates.md",{"path":2563,"priority":274},"references/cosmology.md",{"path":2565,"priority":274},"references/fits.md",{"path":2567,"priority":274},"references/tables.md",{"path":2569,"priority":274},"references/time.md",{"path":2571,"priority":274},"references/units.md",{"path":2573,"priority":274},"references/wcs_and_other_modules.md",{"basePath":2575,"description":2576,"displayName":2577,"installMethods":2578,"rationale":2579,"selectedPaths":2580,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-cirq","Part of the AlterLab Academic Skills suite. Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.","alterlab-cirq",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-cirq/SKILL.md",[2581,2582,2584,2586,2588,2590,2592],{"path":270,"priority":271},{"path":2583,"priority":274},"references/building.md",{"path":2585,"priority":274},"references/experiments.md",{"path":2587,"priority":274},"references/hardware.md",{"path":2589,"priority":274},"references/noise.md",{"path":2591,"priority":274},"references/simulation.md",{"path":2593,"priority":274},"references/transformation.md",{"basePath":2595,"description":2596,"displayName":2597,"installMethods":2598,"rationale":2599,"selectedPaths":2600,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-digital-humanities","Text mining, corpus linguistics, digital archives, GIS for history, network analysis, stylometry, OCR, and data visualization for humanities research. Part of the AlterLab Academic Skills suite.","alterlab-digital-humanities",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-digital-humanities/SKILL.md",[2601,2602],{"path":270,"priority":271},{"path":2603,"priority":274},"references/dh-tools-guide.md",{"basePath":2605,"description":2606,"displayName":2607,"installMethods":2608,"rationale":2609,"selectedPaths":2610,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-fluidsim","Part of the AlterLab Academic Skills suite. Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.","alterlab-fluidsim",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-fluidsim/SKILL.md",[2611,2612,2614,2616,2618,2620,2622],{"path":270,"priority":271},{"path":2613,"priority":274},"references/advanced_features.md",{"path":2615,"priority":274},"references/installation.md",{"path":2617,"priority":274},"references/output_analysis.md",{"path":2619,"priority":274},"references/parameters.md",{"path":2621,"priority":274},"references/simulation_workflow.md",{"path":2623,"priority":274},"references/solvers.md",{"basePath":2625,"description":2626,"displayName":2627,"installMethods":2628,"rationale":2629,"selectedPaths":2630,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-geniml","Part of the AlterLab Academic Skills suite. This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.","alterlab-geniml",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-geniml/SKILL.md",[2631,2632,2634,2636,2638,2640],{"path":270,"priority":271},{"path":2633,"priority":274},"references/bedspace.md",{"path":2635,"priority":274},"references/consensus_peaks.md",{"path":2637,"priority":274},"references/region2vec.md",{"path":2639,"priority":274},"references/scembed.md",{"path":820,"priority":274},{"basePath":2642,"description":2643,"displayName":2644,"installMethods":2645,"rationale":2646,"selectedPaths":2647,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-geomaster","Part of the AlterLab Academic Skills suite. Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.","alterlab-geomaster",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-geomaster/SKILL.md",[2648,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677],{"path":270,"priority":271},{"path":2650,"priority":2349},"README.md",{"path":2652,"priority":274},"references/advanced-gis.md",{"path":2654,"priority":274},"references/big-data.md",{"path":2656,"priority":274},"references/code-examples.md",{"path":2658,"priority":274},"references/coordinate-systems.md",{"path":2660,"priority":274},"references/core-libraries.md",{"path":2662,"priority":274},"references/data-sources.md",{"path":2664,"priority":274},"references/gis-software.md",{"path":2666,"priority":274},"references/industry-applications.md",{"path":2668,"priority":274},"references/machine-learning.md",{"path":2670,"priority":274},"references/programming-languages.md",{"path":2672,"priority":274},"references/remote-sensing.md",{"path":2674,"priority":274},"references/scientific-domains.md",{"path":2676,"priority":274},"references/specialized-topics.md",{"path":2678,"priority":274},"references/troubleshooting.md",{"basePath":2680,"description":2681,"displayName":2682,"installMethods":2683,"rationale":2684,"selectedPaths":2685,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-geopandas","Part of the AlterLab Academic Skills suite. Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.","alterlab-geopandas",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-geopandas/SKILL.md",[2686,2687,2689,2691,2693,2695,2697],{"path":270,"priority":271},{"path":2688,"priority":274},"references/crs-management.md",{"path":2690,"priority":274},"references/data-io.md",{"path":2692,"priority":274},"references/data-structures.md",{"path":2694,"priority":274},"references/geometric-operations.md",{"path":2696,"priority":274},"references/spatial-analysis.md",{"path":416,"priority":274},{"basePath":2699,"description":2700,"displayName":2701,"installMethods":2702,"rationale":2703,"selectedPaths":2704,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-gtars","Part of the AlterLab Academic Skills suite. High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.","alterlab-gtars",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-gtars/SKILL.md",[2705,2706,2708,2710,2712,2714,2716],{"path":270,"priority":271},{"path":2707,"priority":274},"references/cli.md",{"path":2709,"priority":274},"references/coverage.md",{"path":2711,"priority":274},"references/overlap.md",{"path":2713,"priority":274},"references/python-api.md",{"path":2715,"priority":274},"references/refget.md",{"path":1809,"priority":274},{"basePath":2718,"description":2719,"displayName":2720,"installMethods":2721,"rationale":2722,"selectedPaths":2723,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-hypogenic","Part of the AlterLab Academic Skills suite. Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.","alterlab-hypogenic",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-hypogenic/SKILL.md",[2724,2725],{"path":270,"priority":271},{"path":2726,"priority":274},"references/config_template.yaml",{"basePath":2728,"description":2729,"displayName":2730,"installMethods":2731,"rationale":2732,"selectedPaths":2733,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-modal","Part of the AlterLab Academic Skills suite. Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.","alterlab-modal",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-modal/SKILL.md",[2734,2735,2736,2737,2739,2741,2743,2745,2746,2748,2750,2752,2754],{"path":270,"priority":271},{"path":414,"priority":274},{"path":796,"priority":274},{"path":2738,"priority":274},"references/functions.md",{"path":2740,"priority":274},"references/getting-started.md",{"path":2742,"priority":274},"references/gpu.md",{"path":2744,"priority":274},"references/images.md",{"path":1638,"priority":274},{"path":2747,"priority":274},"references/scaling.md",{"path":2749,"priority":274},"references/scheduled-jobs.md",{"path":2751,"priority":274},"references/secrets.md",{"path":2753,"priority":274},"references/volumes.md",{"path":2755,"priority":274},"references/web-endpoints.md",{"basePath":2757,"description":2758,"displayName":2759,"installMethods":2760,"rationale":2761,"selectedPaths":2762,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-pennylane","Part of the AlterLab Academic Skills suite. Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.","alterlab-pennylane",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-pennylane/SKILL.md",[2763,2764,2765,2767,2768,2770,2772,2774],{"path":270,"priority":271},{"path":2613,"priority":274},{"path":2766,"priority":274},"references/devices_backends.md",{"path":1979,"priority":274},{"path":2769,"priority":274},"references/optimization.md",{"path":2771,"priority":274},"references/quantum_chemistry.md",{"path":2773,"priority":274},"references/quantum_circuits.md",{"path":2775,"priority":274},"references/quantum_ml.md",{"basePath":2777,"description":2778,"displayName":2779,"installMethods":2780,"rationale":2781,"selectedPaths":2782,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-pymatgen","Part of the AlterLab Academic Skills suite. Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.","alterlab-pymatgen",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-pymatgen/SKILL.md",[2783,2784,2786,2788,2790,2792,2794,2796,2798],{"path":270,"priority":271},{"path":2785,"priority":274},"references/analysis_modules.md",{"path":2787,"priority":274},"references/core_classes.md",{"path":2789,"priority":274},"references/io_formats.md",{"path":2791,"priority":274},"references/materials_project_api.md",{"path":2793,"priority":274},"references/transformations_workflows.md",{"path":2795,"priority":300},"scripts/phase_diagram_generator.py",{"path":2797,"priority":300},"scripts/structure_analyzer.py",{"path":2799,"priority":300},"scripts/structure_converter.py",{"basePath":2801,"description":2802,"displayName":2803,"installMethods":2804,"rationale":2805,"selectedPaths":2806,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-qiskit","Part of the AlterLab Academic Skills suite. IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.","alterlab-qiskit",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-qiskit/SKILL.md",[2807,2808,2809,2811,2813,2815,2817,2819,2821],{"path":270,"priority":271},{"path":293,"priority":274},{"path":2810,"priority":274},"references/backends.md",{"path":2812,"priority":274},"references/circuits.md",{"path":2814,"priority":274},"references/patterns.md",{"path":2816,"priority":274},"references/primitives.md",{"path":2818,"priority":274},"references/setup.md",{"path":2820,"priority":274},"references/transpilation.md",{"path":416,"priority":274},{"basePath":2823,"description":2824,"displayName":2825,"installMethods":2826,"rationale":2827,"selectedPaths":2828,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-qutip","Part of the AlterLab Academic Skills suite. Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.","alterlab-qutip",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-qutip/SKILL.md",[2829,2830,2831,2833,2834,2836],{"path":270,"priority":271},{"path":310,"priority":274},{"path":2832,"priority":274},"references/analysis.md",{"path":874,"priority":274},{"path":2835,"priority":274},"references/time_evolution.md",{"path":416,"priority":274},{"basePath":2838,"description":2839,"displayName":2840,"installMethods":2841,"rationale":2842,"selectedPaths":2843,"source":283,"sourceLanguage":18,"type":247},"skills/domain-specific/alterlab-social-science-methods","Advanced social science research methods -- discourse analysis, comparative methods, process tracing, participatory research, social network analysis, bibliometrics, and program evaluation. Part of the AlterLab Academic Skills suite.","alterlab-social-science-methods",{"claudeCode":12},"SKILL.md frontmatter at skills/domain-specific/alterlab-social-science-methods/SKILL.md",[2844,2845],{"path":270,"priority":271},{"path":2846,"priority":274},"references/social-science-frameworks.md",{"basePath":2848,"description":2849,"displayName":2850,"installMethods":2851,"rationale":2852,"selectedPaths":2853,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-alpha-vantage","Part of the AlterLab Academic Skills suite. Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.","alterlab-alpha-vantage",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-alpha-vantage/SKILL.md",[2854,2855,2857,2859,2861,2863,2865,2867,2869],{"path":270,"priority":271},{"path":2856,"priority":274},"references/commodities.md",{"path":2858,"priority":274},"references/economic-indicators.md",{"path":2860,"priority":274},"references/forex-crypto.md",{"path":2862,"priority":274},"references/fundamentals.md",{"path":2864,"priority":274},"references/intelligence.md",{"path":2866,"priority":274},"references/options.md",{"path":2868,"priority":274},"references/technical-indicators.md",{"path":2870,"priority":274},"references/time-series.md",{"basePath":2872,"description":2873,"displayName":2874,"installMethods":2875,"rationale":2876,"selectedPaths":2877,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-denario","Part of the AlterLab Academic Skills suite. Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.","alterlab-denario",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-denario/SKILL.md",[2878,2879,2880,2881,2883],{"path":270,"priority":271},{"path":796,"priority":274},{"path":2615,"priority":274},{"path":2882,"priority":274},"references/llm_configuration.md",{"path":2884,"priority":274},"references/research_pipeline.md",{"basePath":2886,"description":2887,"displayName":2888,"installMethods":2889,"rationale":2890,"selectedPaths":2891,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-edgartools","Part of the AlterLab Academic Skills suite. Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.","alterlab-edgartools",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-edgartools/SKILL.md",[2892,2893,2895,2897,2899,2901,2903,2905],{"path":270,"priority":271},{"path":2894,"priority":274},"references/ai-integration.md",{"path":2896,"priority":274},"references/companies.md",{"path":2898,"priority":274},"references/data-objects.md",{"path":2900,"priority":274},"references/entity-facts.md",{"path":2902,"priority":274},"references/filings.md",{"path":2904,"priority":274},"references/financial-data.md",{"path":2906,"priority":274},"references/xbrl.md",{"basePath":2908,"description":2909,"displayName":2910,"installMethods":2911,"rationale":2912,"selectedPaths":2913,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-fred","Part of the AlterLab Academic Skills suite. Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.","alterlab-fred",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-fred/SKILL.md",[2914,2915,2916,2918,2920,2922,2924,2926,2928,2930],{"path":270,"priority":271},{"path":2048,"priority":274},{"path":2917,"priority":274},"references/categories.md",{"path":2919,"priority":274},"references/geofred.md",{"path":2921,"priority":274},"references/releases.md",{"path":2923,"priority":274},"references/series.md",{"path":2925,"priority":274},"references/sources.md",{"path":2927,"priority":274},"references/tags.md",{"path":2929,"priority":300},"scripts/fred_examples.py",{"path":2931,"priority":300},"scripts/fred_query.py",{"basePath":2933,"description":2934,"displayName":2935,"installMethods":2936,"rationale":2937,"selectedPaths":2938,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-hedgefund-monitor","Part of the AlterLab Academic Skills suite. Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.","alterlab-hedgefund-monitor",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-hedgefund-monitor/SKILL.md",[2939,2940,2942,2943,2945,2947,2949,2950],{"path":270,"priority":271},{"path":2941,"priority":274},"references/api-overview.md",{"path":816,"priority":274},{"path":2944,"priority":274},"references/endpoints-combined.md",{"path":2946,"priority":274},"references/endpoints-metadata.md",{"path":2948,"priority":274},"references/endpoints-series-data.md",{"path":796,"priority":274},{"path":2619,"priority":274},{"basePath":2952,"description":2953,"displayName":2954,"installMethods":2955,"rationale":2956,"selectedPaths":2957,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-market-research","Part of the AlterLab Academic Skills suite. Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.","alterlab-market-research",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-market-research/SKILL.md",[2958,2959,2961,2963,2965,2967,2969,2971],{"path":270,"priority":271},{"path":2960,"priority":300},"assets/FORMATTING_GUIDE.md",{"path":2962,"priority":300},"assets/market_report_template.tex",{"path":2964,"priority":300},"assets/market_research.sty",{"path":2966,"priority":274},"references/data_analysis_patterns.md",{"path":2968,"priority":274},"references/report_structure_guide.md",{"path":2970,"priority":274},"references/visual_generation_guide.md",{"path":2972,"priority":300},"scripts/generate_market_visuals.py",{"basePath":2974,"description":2975,"displayName":2976,"installMethods":2977,"rationale":2978,"selectedPaths":2979,"source":283,"sourceLanguage":18,"type":247},"skills/finance-economics/alterlab-usfiscaldata","Part of the AlterLab Academic Skills suite. Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.","alterlab-usfiscaldata",{"claudeCode":12},"SKILL.md frontmatter at skills/finance-economics/alterlab-usfiscaldata/SKILL.md",[2980,2981,2983,2985,2987,2989,2991,2992,2993],{"path":270,"priority":271},{"path":2982,"priority":274},"references/api-basics.md",{"path":2984,"priority":274},"references/datasets-debt.md",{"path":2986,"priority":274},"references/datasets-fiscal.md",{"path":2988,"priority":274},"references/datasets-interest-rates.md",{"path":2990,"priority":274},"references/datasets-securities.md",{"path":796,"priority":274},{"path":2619,"priority":274},{"path":2994,"priority":274},"references/response-format.md",{"basePath":2996,"description":2997,"displayName":2998,"installMethods":2999,"rationale":3000,"selectedPaths":3001,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-benchling","Part of the AlterLab Academic Skills suite. Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.","alterlab-benchling",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-benchling/SKILL.md",[3002,3003,3004,3006],{"path":270,"priority":271},{"path":2034,"priority":274},{"path":3005,"priority":274},"references/authentication.md",{"path":3007,"priority":274},"references/sdk_reference.md",{"basePath":3009,"description":3010,"displayName":3011,"installMethods":3012,"rationale":3013,"selectedPaths":3014,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-dnanexus","Part of the AlterLab Academic Skills suite. DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.","alterlab-dnanexus",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-dnanexus/SKILL.md",[3015,3016,3018,3019,3021,3023],{"path":270,"priority":271},{"path":3017,"priority":274},"references/app-development.md",{"path":2421,"priority":274},{"path":3020,"priority":274},"references/data-operations.md",{"path":3022,"priority":274},"references/job-execution.md",{"path":3024,"priority":274},"references/python-sdk.md",{"basePath":3026,"description":3027,"displayName":3028,"installMethods":3029,"rationale":3030,"selectedPaths":3031,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-ginkgo-cloud","Part of the AlterLab Academic Skills suite. Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run cell-free protein expression (validation or optimization), generate fluorescent pixel art, or interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.","alterlab-ginkgo-cloud",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-ginkgo-cloud/SKILL.md",[3032,3033,3035,3037],{"path":270,"priority":271},{"path":3034,"priority":274},"references/cell-free-protein-expression-optimization.md",{"path":3036,"priority":274},"references/cell-free-protein-expression-validation.md",{"path":3038,"priority":274},"references/fluorescent-pixel-art-generation.md",{"basePath":3040,"description":3041,"displayName":3042,"installMethods":3043,"rationale":3044,"selectedPaths":3045,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-labarchive","Part of the AlterLab Academic Skills suite. Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.","alterlab-labarchive",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-labarchive/SKILL.md",[3046,3047,3048,3050,3051,3053,3055],{"path":270,"priority":271},{"path":414,"priority":274},{"path":3049,"priority":274},"references/authentication_guide.md",{"path":492,"priority":274},{"path":3052,"priority":300},"scripts/entry_operations.py",{"path":3054,"priority":300},"scripts/notebook_operations.py",{"path":3056,"priority":300},"scripts/setup_config.py",{"basePath":3058,"description":3059,"displayName":3060,"installMethods":3061,"rationale":3062,"selectedPaths":3063,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-latchbio","Part of the AlterLab Academic Skills suite. Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.","alterlab-latchbio",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-latchbio/SKILL.md",[3064,3065,3066,3068,3070],{"path":270,"priority":271},{"path":490,"priority":274},{"path":3067,"priority":274},"references/resource-configuration.md",{"path":3069,"priority":274},"references/verified-workflows.md",{"path":3071,"priority":274},"references/workflow-creation.md",{"basePath":3073,"description":3074,"displayName":3075,"installMethods":3076,"rationale":3077,"selectedPaths":3078,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-omero","Part of the AlterLab Academic Skills suite. Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.","alterlab-omero",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-omero/SKILL.md",[3079,3080,3081,3083,3085,3087,3089,3091,3093],{"path":270,"priority":271},{"path":310,"priority":274},{"path":3082,"priority":274},"references/connection.md",{"path":3084,"priority":274},"references/data_access.md",{"path":3086,"priority":274},"references/image_processing.md",{"path":3088,"priority":274},"references/metadata.md",{"path":3090,"priority":274},"references/rois.md",{"path":3092,"priority":274},"references/scripts.md",{"path":2567,"priority":274},{"basePath":3095,"description":3096,"displayName":3097,"installMethods":3098,"rationale":3099,"selectedPaths":3100,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-opentrons","Part of the AlterLab Academic Skills suite. Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.","alterlab-opentrons",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-opentrons/SKILL.md",[3101,3102,3103,3105,3107],{"path":270,"priority":271},{"path":414,"priority":274},{"path":3104,"priority":300},"scripts/basic_protocol_template.py",{"path":3106,"priority":300},"scripts/pcr_setup_template.py",{"path":3108,"priority":300},"scripts/serial_dilution_template.py",{"basePath":3110,"description":3111,"displayName":3112,"installMethods":3113,"rationale":3114,"selectedPaths":3115,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-protocolsio","Part of the AlterLab Academic Skills suite. Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.","alterlab-protocolsio",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-protocolsio/SKILL.md",[3116,3117,3119,3120,3122,3124,3126],{"path":270,"priority":271},{"path":3118,"priority":274},"references/additional_features.md",{"path":3005,"priority":274},{"path":3121,"priority":274},"references/discussions.md",{"path":3123,"priority":274},"references/file_manager.md",{"path":3125,"priority":274},"references/protocols_api.md",{"path":3127,"priority":274},"references/workspaces.md",{"basePath":3129,"description":3130,"displayName":3131,"installMethods":3132,"rationale":3133,"selectedPaths":3134,"source":283,"sourceLanguage":18,"type":247},"skills/lab-integrations/alterlab-pylabrobot","Part of the AlterLab Academic Skills suite. Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.","alterlab-pylabrobot",{"claudeCode":12},"SKILL.md frontmatter at skills/lab-integrations/alterlab-pylabrobot/SKILL.md",[3135,3136,3138,3140,3142,3144,3145],{"path":270,"priority":271},{"path":3137,"priority":274},"references/analytical-equipment.md",{"path":3139,"priority":274},"references/hardware-backends.md",{"path":3141,"priority":274},"references/liquid-handling.md",{"path":3143,"priority":274},"references/material-handling.md",{"path":1638,"priority":274},{"path":416,"priority":274},{"basePath":3147,"description":3148,"displayName":3149,"installMethods":3150,"rationale":3151,"selectedPaths":3152,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-bgpt-search","Part of the AlterLab Academic Skills suite. Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.","alterlab-bgpt-search",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-bgpt-search/SKILL.md",[3153],{"path":270,"priority":271},{"basePath":3155,"description":3156,"displayName":3157,"installMethods":3158,"rationale":3159,"selectedPaths":3160,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-mixed-methods","Mixed methods research design and integration strategies for combining qualitative and quantitative approaches. Use when planning convergent, explanatory sequential, exploratory sequential, embedded, transformative, or multiphase designs; when integrating diverse data sources through merging, connecting, or embedding; when constructing joint displays or meta-inferences; or when evaluating quality criteria specific to mixed methods research. Covers Creswell & Plano Clark frameworks, notation systems, and software tools for integration. Part of the AlterLab Academic Skills suite.","alterlab-mixed-methods",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-mixed-methods/SKILL.md",[3161,3162],{"path":270,"priority":271},{"path":3163,"priority":274},"references/mixed-methods-frameworks.md",{"basePath":3165,"description":3166,"displayName":3167,"installMethods":3168,"rationale":3169,"selectedPaths":3170,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-open-science","Guidance for open science practices -- preregistration, open data, reproducible analysis, open access publishing, and FAIR principles. Part of the AlterLab Academic Skills suite.","alterlab-open-science",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-open-science/SKILL.md",[3171,3172],{"path":270,"priority":271},{"path":3173,"priority":274},"references/open-science-resources.md",{"basePath":3175,"description":3176,"displayName":3177,"installMethods":3178,"rationale":3179,"selectedPaths":3180,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-parallel-web","Part of the AlterLab Academic Skills suite. Search the web, extract URL content, and run deep research using the Parallel Chat API and Extract API. Use for ALL web searches, research queries, and general information gathering. Provides synthesized summaries with citations.","alterlab-parallel-web",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-parallel-web/SKILL.md",[3181,3182,3183,3185,3187,3189,3191],{"path":270,"priority":271},{"path":414,"priority":274},{"path":3184,"priority":274},"references/deep_research_guide.md",{"path":3186,"priority":274},"references/extraction_patterns.md",{"path":3188,"priority":274},"references/search_best_practices.md",{"path":3190,"priority":274},"references/workflow_recipes.md",{"path":3192,"priority":300},"scripts/parallel_web.py",{"basePath":3194,"description":3195,"displayName":3196,"installMethods":3197,"rationale":3198,"selectedPaths":3199,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-perplexity","Part of the AlterLab Academic Skills suite. Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.","alterlab-perplexity",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-perplexity/SKILL.md",[3200,3201,3203,3205,3207,3209,3211],{"path":270,"priority":271},{"path":3202,"priority":300},"assets/.env.example",{"path":3204,"priority":274},"references/model_comparison.md",{"path":3206,"priority":274},"references/openrouter_setup.md",{"path":3208,"priority":274},"references/search_strategies.md",{"path":3210,"priority":300},"scripts/perplexity_search.py",{"path":3212,"priority":300},"scripts/setup_env.py",{"basePath":3214,"description":3215,"displayName":3216,"installMethods":3217,"rationale":3218,"selectedPaths":3219,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-pyzotero","Part of the AlterLab Academic Skills suite. Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.","alterlab-pyzotero",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-pyzotero/SKILL.md",[3220,3221,3222,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3242],{"path":270,"priority":271},{"path":3005,"priority":274},{"path":2707,"priority":274},{"path":3224,"priority":274},"references/collections.md",{"path":3226,"priority":274},"references/error-handling.md",{"path":3228,"priority":274},"references/exports.md",{"path":3230,"priority":274},"references/files-attachments.md",{"path":3232,"priority":274},"references/full-text.md",{"path":3234,"priority":274},"references/pagination.md",{"path":3236,"priority":274},"references/read-api.md",{"path":3238,"priority":274},"references/saved-searches.md",{"path":3240,"priority":274},"references/search-params.md",{"path":2927,"priority":274},{"path":3243,"priority":274},"references/write-api.md",{"basePath":3245,"description":3246,"displayName":3247,"installMethods":3248,"rationale":3249,"selectedPaths":3250,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-qualitative-methods","Part of the AlterLab Academic Skills suite for faculty and researchers. Comprehensive qualitative research methods assistant. Supports thematic analysis (Braun & Clarke), grounded theory (Strauss & Corbin; Charmaz), interpretative phenomenological analysis (IPA), content analysis, narrative inquiry, ethnography, case study methodology (Yin), coding techniques (open/axial/selective), NVivo-style workflows with Python alternatives, trustworthiness criteria (Lincoln & Guba), reflexivity, and member checking. Triggers on: qualitative research, thematic analysis, grounded theory, coding data, phenomenology, IPA, ethnography, case study, narrative inquiry, content analysis, qualitative coding, NVivo, trustworthiness, member checking, reflexivity, interview analysis, focus group analysis.","alterlab-qualitative-methods",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-qualitative-methods/SKILL.md",[3251,3252],{"path":270,"priority":271},{"path":3253,"priority":274},"references/qualitative-frameworks.md",{"basePath":3255,"description":3256,"displayName":3257,"installMethods":3258,"rationale":3259,"selectedPaths":3260,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-research-ethics","Part of the AlterLab Academic Skills suite for faculty and researchers. Comprehensive research ethics and compliance assistant. Supports IRB/ethics board applications, informed consent drafting, data management plans, Belmont Report principles, GDPR compliance for research, HIPAA considerations, vulnerable populations protocols, deception research, confidentiality and anonymity, research integrity (fabrication/falsification/plagiarism), conflict of interest disclosure, and dual-use research oversight. Triggers on: research ethics, IRB, ethics board, informed consent, data management plan, Belmont Report, GDPR research, HIPAA research, vulnerable populations, research integrity, plagiarism, conflict of interest, ethics application, ethical review, human subjects, animal ethics.","alterlab-research-ethics",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-research-ethics/SKILL.md",[3261,3262],{"path":270,"priority":271},{"path":3263,"priority":274},"references/ethics-guidelines.md",{"basePath":3265,"description":3266,"displayName":3267,"installMethods":3268,"rationale":3269,"selectedPaths":3270,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-research-lookup","Part of the AlterLab Academic Skills suite. Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.","alterlab-research-lookup",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-research-lookup/SKILL.md",[3271,3272,3273],{"path":270,"priority":271},{"path":2650,"priority":2349},{"path":3274,"priority":300},"scripts/research_lookup.py",{"basePath":3276,"description":3277,"displayName":3278,"installMethods":3279,"rationale":3280,"selectedPaths":3281,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-scientific-brainstorm","Part of the AlterLab Academic Skills suite. Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","alterlab-scientific-brainstorm",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-scientific-brainstorm/SKILL.md",[3282,3283],{"path":270,"priority":271},{"path":3284,"priority":274},"references/brainstorming_methods.md",{"basePath":3286,"description":3287,"displayName":3288,"installMethods":3289,"rationale":3290,"selectedPaths":3291,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-scientific-thinking","Part of the AlterLab Academic Skills suite. Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.","alterlab-scientific-thinking",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-scientific-thinking/SKILL.md",[3292,3293,3295,3297,3299,3300,3302],{"path":270,"priority":271},{"path":3294,"priority":274},"references/common_biases.md",{"path":3296,"priority":274},"references/evidence_hierarchy.md",{"path":3298,"priority":274},"references/experimental_design.md",{"path":1202,"priority":274},{"path":3301,"priority":274},"references/scientific_method.md",{"path":3303,"priority":274},"references/statistical_pitfalls.md",{"basePath":3305,"description":3306,"displayName":3307,"installMethods":3308,"rationale":3309,"selectedPaths":3310,"source":283,"sourceLanguage":18,"type":247},"skills/research-tools/alterlab-survey-design","Part of the AlterLab Academic Skills suite for faculty and researchers. Comprehensive survey and instrument design assistant. Supports questionnaire construction, Likert scale design, question types (open/closed/matrix), response bias mitigation, sampling strategies (probability/non-probability), pilot testing, instrument validation (Cronbach's alpha, factor analysis), online survey tools (Qualtrics, REDCap, Google Forms), interview protocol development, focus group facilitation, mixed-mode surveys, and cultural adaptation of instruments. Triggers on: survey design, questionnaire, Likert scale, sampling strategy, pilot testing, instrument validation, Cronbach's alpha, factor analysis, interview protocol, focus group, Qualtrics, REDCap, survey bias, response rate, questionnaire construction, scale development.","alterlab-survey-design",{"claudeCode":12},"SKILL.md frontmatter at skills/research-tools/alterlab-survey-design/SKILL.md",[3311,3312],{"path":270,"priority":271},{"path":3313,"priority":274},"references/survey-methodology.md",{"basePath":3315,"description":3316,"displayName":3317,"installMethods":3318,"rationale":3319,"selectedPaths":3320,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-generate-image","Part of the AlterLab Academic Skills suite. Generate or edit images using AI models (FLUX, Nano Banana 2). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.","alterlab-generate-image",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-generate-image/SKILL.md",[3321,3322],{"path":270,"priority":271},{"path":3323,"priority":300},"scripts/generate_image.py",{"basePath":3325,"description":3326,"displayName":3327,"installMethods":3328,"rationale":3329,"selectedPaths":3330,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-infographics","Part of the AlterLab Academic Skills suite. Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.","alterlab-infographics",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-infographics/SKILL.md",[3331,3332,3334,3336,3338,3340],{"path":270,"priority":271},{"path":3333,"priority":274},"references/color_palettes.md",{"path":3335,"priority":274},"references/design_principles.md",{"path":3337,"priority":274},"references/infographic_types.md",{"path":3339,"priority":300},"scripts/generate_infographic.py",{"path":3341,"priority":300},"scripts/generate_infographic_ai.py",{"basePath":3343,"description":3344,"displayName":3345,"installMethods":3346,"rationale":3347,"selectedPaths":3348,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-matplotlib","Part of the AlterLab Academic Skills suite. Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.","alterlab-matplotlib",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-matplotlib/SKILL.md",[3349,3350,3351,3353,3355,3357,3359],{"path":270,"priority":271},{"path":414,"priority":274},{"path":3352,"priority":274},"references/common_issues.md",{"path":3354,"priority":274},"references/plot_types.md",{"path":3356,"priority":274},"references/styling_guide.md",{"path":3358,"priority":300},"scripts/plot_template.py",{"path":3360,"priority":300},"scripts/style_configurator.py",{"basePath":3362,"description":3363,"displayName":3364,"installMethods":3365,"rationale":3366,"selectedPaths":3367,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-mermaid","Part of the AlterLab Academic Skills suite. Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","alterlab-mermaid",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-mermaid/SKILL.md",[3368,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439],{"path":270,"priority":271},{"path":3370,"priority":300},"assets/examples/example-research-report.md",{"path":3372,"priority":274},"references/diagrams/architecture.md",{"path":3374,"priority":274},"references/diagrams/block.md",{"path":3376,"priority":274},"references/diagrams/c4.md",{"path":3378,"priority":274},"references/diagrams/class.md",{"path":3380,"priority":274},"references/diagrams/complex_examples.md",{"path":3382,"priority":274},"references/diagrams/er.md",{"path":3384,"priority":274},"references/diagrams/flowchart.md",{"path":3386,"priority":274},"references/diagrams/gantt.md",{"path":3388,"priority":274},"references/diagrams/git_graph.md",{"path":3390,"priority":274},"references/diagrams/kanban.md",{"path":3392,"priority":274},"references/diagrams/mindmap.md",{"path":3394,"priority":274},"references/diagrams/packet.md",{"path":3396,"priority":274},"references/diagrams/pie.md",{"path":3398,"priority":274},"references/diagrams/quadrant.md",{"path":3400,"priority":274},"references/diagrams/radar.md",{"path":3402,"priority":274},"references/diagrams/requirement.md",{"path":3404,"priority":274},"references/diagrams/sankey.md",{"path":3406,"priority":274},"references/diagrams/sequence.md",{"path":3408,"priority":274},"references/diagrams/state.md",{"path":3410,"priority":274},"references/diagrams/timeline.md",{"path":3412,"priority":274},"references/diagrams/treemap.md",{"path":3414,"priority":274},"references/diagrams/user_journey.md",{"path":3416,"priority":274},"references/diagrams/xy_chart.md",{"path":3418,"priority":274},"references/diagrams/zenuml.md",{"path":3420,"priority":274},"references/markdown_style_guide.md",{"path":3422,"priority":274},"references/mermaid_style_guide.md",{"path":3424,"priority":300},"templates/decision_record.md",{"path":3426,"priority":300},"templates/how_to_guide.md",{"path":3428,"priority":300},"templates/issue.md",{"path":3430,"priority":300},"templates/kanban.md",{"path":3432,"priority":300},"templates/presentation.md",{"path":3434,"priority":300},"templates/project_documentation.md",{"path":3436,"priority":300},"templates/pull_request.md",{"path":3438,"priority":300},"templates/research_paper.md",{"path":3440,"priority":300},"templates/status_report.md",{"basePath":3442,"description":3443,"displayName":3444,"installMethods":3445,"rationale":3446,"selectedPaths":3447,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-plotly","Part of the AlterLab Academic Skills suite. Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.","alterlab-plotly",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-plotly/SKILL.md",[3448,3449,3451,3453,3455,3457],{"path":270,"priority":271},{"path":3450,"priority":274},"references/chart-types.md",{"path":3452,"priority":274},"references/export-interactivity.md",{"path":3454,"priority":274},"references/graph-objects.md",{"path":3456,"priority":274},"references/layouts-styling.md",{"path":3458,"priority":274},"references/plotly-express.md",{"basePath":3460,"description":3461,"displayName":3462,"installMethods":3463,"rationale":3464,"selectedPaths":3465,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-scientific-schematics","Part of the AlterLab Academic Skills suite. Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.","alterlab-scientific-schematics",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-scientific-schematics/SKILL.md",[3466,3467,3469,3470,3471,3473,3475],{"path":270,"priority":271},{"path":3468,"priority":274},"references/QUICK_REFERENCE.md",{"path":911,"priority":274},{"path":273,"priority":274},{"path":3472,"priority":300},"scripts/example_usage.sh",{"path":3474,"priority":300},"scripts/generate_schematic.py",{"path":3476,"priority":300},"scripts/generate_schematic_ai.py",{"basePath":3478,"description":3479,"displayName":3480,"installMethods":3481,"rationale":3482,"selectedPaths":3483,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-scientific-viz","Part of the AlterLab Academic Skills suite. Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.","alterlab-scientific-viz",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-scientific-viz/SKILL.md",[3484,3485,3487,3489,3491,3493,3494,3496,3498,3500,3502],{"path":270,"priority":271},{"path":3486,"priority":300},"assets/color_palettes.py",{"path":3488,"priority":300},"assets/nature.mplstyle",{"path":3490,"priority":300},"assets/presentation.mplstyle",{"path":3492,"priority":300},"assets/publication.mplstyle",{"path":3333,"priority":274},{"path":3495,"priority":274},"references/journal_requirements.md",{"path":3497,"priority":274},"references/matplotlib_examples.md",{"path":3499,"priority":274},"references/publication_guidelines.md",{"path":3501,"priority":300},"scripts/figure_export.py",{"path":3503,"priority":300},"scripts/style_presets.py",{"basePath":3505,"description":3506,"displayName":3507,"installMethods":3508,"rationale":3509,"selectedPaths":3510,"source":283,"sourceLanguage":18,"type":247},"skills/visualization/alterlab-seaborn","Part of the AlterLab Academic Skills suite. Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.","alterlab-seaborn",{"claudeCode":12},"SKILL.md frontmatter at skills/visualization/alterlab-seaborn/SKILL.md",[3511,3512,3513,3515],{"path":270,"priority":271},{"path":796,"priority":274},{"path":3514,"priority":274},"references/function_reference.md",{"path":3516,"priority":274},"references/objects_interface.md",{"basePath":3518,"description":3519,"displayName":3520,"installMethods":3521,"rationale":3522,"selectedPaths":3523,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-academic-career","Academic career document preparation and professional development for faculty and researchers. Use when drafting academic CVs, research statements, teaching philosophies, diversity statements, cover letters for faculty positions, tenure dossiers, promotion narratives, or academic portfolios. Also covers conference networking strategies, building academic web presence, understanding impact metrics, ORCID profiles, and writing mentorship statements. Part of the AlterLab Academic Skills suite.","alterlab-academic-career",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-academic-career/SKILL.md",[3524,3525],{"path":270,"priority":271},{"path":3526,"priority":274},"references/career-templates.md",{"basePath":3528,"description":3529,"displayName":3530,"installMethods":3531,"rationale":3532,"selectedPaths":3533,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-citation-mgmt","Part of the AlterLab Academic Skills suite. Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.","alterlab-citation-mgmt",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-citation-mgmt/SKILL.md",[3534,3535,3537,3539,3541,3543,3545,3547,3549,3551,3552,3554,3556,3558],{"path":270,"priority":271},{"path":3536,"priority":300},"assets/bibtex_template.bib",{"path":3538,"priority":300},"assets/citation_checklist.md",{"path":3540,"priority":274},"references/bibtex_formatting.md",{"path":3542,"priority":274},"references/citation_validation.md",{"path":3544,"priority":274},"references/google_scholar_search.md",{"path":3546,"priority":274},"references/metadata_extraction.md",{"path":3548,"priority":274},"references/pubmed_search.md",{"path":3550,"priority":300},"scripts/doi_to_bibtex.py",{"path":1091,"priority":300},{"path":3553,"priority":300},"scripts/format_bibtex.py",{"path":3555,"priority":300},"scripts/search_google_scholar.py",{"path":3557,"priority":300},"scripts/search_pubmed.py",{"path":3559,"priority":300},"scripts/validate_citations.py",{"basePath":3561,"description":3562,"displayName":3563,"installMethods":3564,"rationale":3565,"selectedPaths":3566,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-hypothesis-gen","Part of the AlterLab Academic Skills suite. Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.","alterlab-hypothesis-gen",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-hypothesis-gen/SKILL.md",[3567,3568,3569,3571,3573,3575,3577],{"path":270,"priority":271},{"path":2960,"priority":300},{"path":3570,"priority":300},"assets/hypothesis_generation.sty",{"path":3572,"priority":300},"assets/hypothesis_report_template.tex",{"path":3574,"priority":274},"references/experimental_design_patterns.md",{"path":3576,"priority":274},"references/hypothesis_quality_criteria.md",{"path":3578,"priority":274},"references/literature_search_strategies.md",{"basePath":3580,"description":3581,"displayName":3582,"installMethods":3583,"rationale":3584,"selectedPaths":3585,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-latex-posters","Part of the AlterLab Academic Skills suite. Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.","alterlab-latex-posters",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-latex-posters/SKILL.md",[3586,3587,3589,3591,3593,3595,3596,3598,3600,3602,3604],{"path":270,"priority":271},{"path":3588,"priority":300},"assets/baposter_template.tex",{"path":3590,"priority":300},"assets/beamerposter_template.tex",{"path":3592,"priority":300},"assets/poster_quality_checklist.md",{"path":3594,"priority":300},"assets/tikzposter_template.tex",{"path":911,"priority":274},{"path":3597,"priority":274},"references/latex_poster_packages.md",{"path":3599,"priority":274},"references/poster_content_guide.md",{"path":3601,"priority":274},"references/poster_design_principles.md",{"path":3603,"priority":274},"references/poster_layout_design.md",{"path":3605,"priority":300},"scripts/review_poster.sh",{"basePath":3607,"description":3608,"displayName":3609,"installMethods":3610,"rationale":3611,"selectedPaths":3612,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-literature-review","Part of the AlterLab Academic Skills suite. Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).","alterlab-literature-review",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-literature-review/SKILL.md",[3613,3614,3616,3618,3620,3622,3624],{"path":270,"priority":271},{"path":3615,"priority":300},"assets/review_template.md",{"path":3617,"priority":274},"references/citation_styles.md",{"path":3619,"priority":274},"references/database_strategies.md",{"path":3621,"priority":300},"scripts/generate_pdf.py",{"path":3623,"priority":300},"scripts/search_databases.py",{"path":3625,"priority":300},"scripts/verify_citations.py",{"basePath":3627,"description":3628,"displayName":3629,"installMethods":3630,"rationale":3631,"selectedPaths":3632,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-paper-2-web","Part of the AlterLab Academic Skills suite. This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.","alterlab-paper-2-web",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-paper-2-web/SKILL.md",[3633,3634,3635,3637,3639,3641],{"path":270,"priority":271},{"path":2615,"priority":274},{"path":3636,"priority":274},"references/paper2poster.md",{"path":3638,"priority":274},"references/paper2video.md",{"path":3640,"priority":274},"references/paper2web.md",{"path":3642,"priority":274},"references/usage_examples.md",{"basePath":3644,"description":3645,"displayName":3646,"installMethods":3647,"rationale":3648,"selectedPaths":3649,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-peer-review","Part of the AlterLab Academic Skills suite. Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.","alterlab-peer-review",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-peer-review/SKILL.md",[3650,3651,3652],{"path":270,"priority":271},{"path":3352,"priority":274},{"path":1678,"priority":274},{"basePath":3654,"description":3655,"displayName":3656,"installMethods":3657,"rationale":3658,"selectedPaths":3659,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-pptx-posters","Part of the AlterLab Academic Skills suite. Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.","alterlab-pptx-posters",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-pptx-posters/SKILL.md",[3660,3661,3663,3664,3665,3666],{"path":270,"priority":271},{"path":3662,"priority":300},"assets/poster_html_template.html",{"path":3592,"priority":300},{"path":3599,"priority":274},{"path":3601,"priority":274},{"path":3603,"priority":274},{"basePath":3668,"description":3669,"displayName":3670,"installMethods":3671,"rationale":3672,"selectedPaths":3673,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-research-grants","Part of the AlterLab Academic Skills suite. Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.","alterlab-research-grants",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-research-grants/SKILL.md",[3674,3675,3677,3679,3681,3682,3684,3686,3688,3690,3692,3694],{"path":270,"priority":271},{"path":3676,"priority":300},"assets/budget_justification_template.md",{"path":3678,"priority":300},"assets/nih_specific_aims_template.md",{"path":3680,"priority":300},"assets/nsf_project_summary_template.md",{"path":911,"priority":274},{"path":3683,"priority":274},"references/broader_impacts.md",{"path":3685,"priority":274},"references/darpa_guidelines.md",{"path":3687,"priority":274},"references/doe_guidelines.md",{"path":3689,"priority":274},"references/nih_guidelines.md",{"path":3691,"priority":274},"references/nsf_guidelines.md",{"path":3693,"priority":274},"references/nstc_guidelines.md",{"path":3695,"priority":274},"references/specific_aims_guide.md",{"basePath":3697,"description":3698,"displayName":3699,"installMethods":3700,"rationale":3701,"selectedPaths":3702,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-scholar-eval","Part of the AlterLab Academic Skills suite. Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.","alterlab-scholar-eval",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-scholar-eval/SKILL.md",[3703,3704,3706],{"path":270,"priority":271},{"path":3705,"priority":274},"references/evaluation_framework.md",{"path":3707,"priority":300},"scripts/calculate_scores.py",{"basePath":3709,"description":3710,"displayName":3711,"installMethods":3712,"rationale":3713,"selectedPaths":3714,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-scientific-slides","Part of the AlterLab Academic Skills suite. Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.","alterlab-scientific-slides",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-scientific-slides/SKILL.md",[3715,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746],{"path":270,"priority":271},{"path":3717,"priority":300},"assets/beamer_template_conference.tex",{"path":3719,"priority":300},"assets/beamer_template_defense.tex",{"path":3721,"priority":300},"assets/beamer_template_seminar.tex",{"path":3723,"priority":300},"assets/powerpoint_design_guide.md",{"path":3725,"priority":300},"assets/timing_guidelines.md",{"path":3727,"priority":274},"references/beamer_guide.md",{"path":3729,"priority":274},"references/data_visualization_slides.md",{"path":3731,"priority":274},"references/presentation_structure.md",{"path":3733,"priority":274},"references/slide_design_principles.md",{"path":3735,"priority":274},"references/talk_types_guide.md",{"path":3737,"priority":274},"references/visual_review_workflow.md",{"path":3739,"priority":300},"scripts/generate_slide_image.py",{"path":3741,"priority":300},"scripts/generate_slide_image_ai.py",{"path":3743,"priority":300},"scripts/pdf_to_images.py",{"path":3745,"priority":300},"scripts/slides_to_pdf.py",{"path":3747,"priority":300},"scripts/validate_presentation.py",{"basePath":3749,"description":3750,"displayName":3751,"installMethods":3752,"rationale":3753,"selectedPaths":3754,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-scientific-writing","Part of the AlterLab Academic Skills suite. Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.","alterlab-scientific-writing",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-scientific-writing/SKILL.md",[3755,3756,3758,3760,3762,3763,3765,3767,3769,3771],{"path":270,"priority":271},{"path":3757,"priority":300},"assets/REPORT_FORMATTING_GUIDE.md",{"path":3759,"priority":300},"assets/scientific_report.sty",{"path":3761,"priority":300},"assets/scientific_report_template.tex",{"path":3617,"priority":274},{"path":3764,"priority":274},"references/figures_tables.md",{"path":3766,"priority":274},"references/imrad_structure.md",{"path":3768,"priority":274},"references/professional_report_formatting.md",{"path":3770,"priority":274},"references/reporting_guidelines.md",{"path":3772,"priority":274},"references/writing_principles.md",{"basePath":3774,"description":3775,"displayName":3776,"installMethods":3777,"rationale":3778,"selectedPaths":3779,"source":283,"sourceLanguage":18,"type":247},"skills/writing-tools/alterlab-venue-templates","Part of the AlterLab Academic Skills suite. Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","alterlab-venue-templates",{"claudeCode":12},"SKILL.md frontmatter at skills/writing-tools/alterlab-venue-templates/SKILL.md",[3780,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827],{"path":270,"priority":271},{"path":3782,"priority":300},"assets/examples/cell_summary_example.md",{"path":3784,"priority":300},"assets/examples/medical_structured_abstract.md",{"path":3786,"priority":300},"assets/examples/nature_abstract_examples.md",{"path":3788,"priority":300},"assets/examples/neurips_introduction_example.md",{"path":3790,"priority":300},"assets/grants/nih_specific_aims.tex",{"path":3792,"priority":300},"assets/grants/nsf_proposal_template.tex",{"path":3794,"priority":300},"assets/journals/nature_article.tex",{"path":3796,"priority":300},"assets/journals/neurips_article.tex",{"path":3798,"priority":300},"assets/journals/plos_one.tex",{"path":3800,"priority":300},"assets/posters/beamerposter_academic.tex",{"path":3802,"priority":274},"references/cell_press_style.md",{"path":3804,"priority":274},"references/conferences_formatting.md",{"path":3806,"priority":274},"references/cs_conference_style.md",{"path":3808,"priority":274},"references/grants_requirements.md",{"path":3810,"priority":274},"references/journals_formatting.md",{"path":3812,"priority":274},"references/medical_journal_styles.md",{"path":3814,"priority":274},"references/ml_conference_style.md",{"path":3816,"priority":274},"references/nature_science_style.md",{"path":3818,"priority":274},"references/posters_guidelines.md",{"path":3820,"priority":274},"references/reviewer_expectations.md",{"path":3822,"priority":274},"references/venue_writing_styles.md",{"path":3824,"priority":300},"scripts/customize_template.py",{"path":3826,"priority":300},"scripts/query_template.py",{"path":3828,"priority":300},"scripts/validate_format.py",{"sources":3830},[3831],"manual",{"npmPackage":3833},"alterlab-academic-skills",{"closedIssues90d":8,"description":3835,"forks":8,"license":238,"openIssues90d":233,"pushedAt":234,"readmeSize":230,"stars":235,"topics":3836},"🧬 186+ Claude AI skills for faculty members and academic researchers — organized by research domain. Transform Claude into your domain-specific research expert.",[3837,3838,3839,3840,3841,3842,216,3843,3844,3845,3846],"academic-research","ai-skills","anthropic","bioinformatics","claude-ai","claude-projects","prompt-engineering","research-tools","scientific-computing","scientific-writing",{"downloads":8},{"classifiedAt":3849,"discoverAt":3850,"extractAt":3851,"githubAt":3851,"npmAt":3852,"updatedAt":3849},1778675144889,1778675137519,1778675141295,1778675142901,[216,212,214,215,213],{"evaluatedAt":241,"extractAt":3855,"updatedAt":241},1778675145461,[],[3858,3886,3916,3937,3968,3989],{"_creationTime":3859,"_id":3860,"community":3861,"display":3862,"identity":3867,"providers":3872,"relations":3880,"tags":3882,"workflow":3883},1778691799740.4976,"k1719vgzsxtv8exr684y5ww47s86mzqh",{"reviewCount":8},{"description":3863,"installMethods":3864,"name":13,"sourceUrl":3866},"Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.",{"claudeCode":3865},"K-Dense-AI/claude-scientific-skills","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":3868,"githubOwner":3869,"githubRepo":3870,"locale":18,"slug":3871,"type":247},"scientific-skills/timesfm-forecasting","K-Dense-AI","claude-scientific-skills","timesfm-forecasting",{"evaluate":3873,"extract":3879},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3874,"tags":3875,"targetMarket":217,"tier":218},100,[213,212,3876,214,3877,3878,215],"univariate","timesfm","machine-learning",{"commitSha":253,"license":238},{"repoId":3881},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[212,214,3878,215,213,3877,3876],{"evaluatedAt":3884,"extractAt":3885,"updatedAt":3884},1778694590335,1778691799740,{"_creationTime":3887,"_id":3888,"community":3889,"display":3890,"identity":3896,"providers":3901,"relations":3909,"tags":3911,"workflow":3912},1778698837670.8,"k17a19x757qjaehqa5jah8k7y986n55p",{"reviewCount":8},{"description":3891,"installMethods":3892,"name":3894,"sourceUrl":3895},"Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.",{"claudeCode":3893},"Whatsonyourmind/oraclaw","OraClaw Forecast","https://github.com/Whatsonyourmind/oraclaw",{"basePath":3897,"githubOwner":3898,"githubRepo":3899,"locale":18,"slug":3900,"type":247},"mission-control/packages/clawhub-skills/oraclaw-forecast","Whatsonyourmind","oraclaw","oraclaw-forecast",{"evaluate":3902,"extract":3908},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3874,"tags":3903,"targetMarket":217,"tier":218},[212,213,3904,3905,3906,3907,216],"prediction","arima","holt-winters","analytics",{"commitSha":253,"license":238},{"repoId":3910},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",[3907,3905,216,212,3906,3904,213],{"evaluatedAt":3913,"extractAt":3914,"updatedAt":3915},1778698975269,1778698837670,1778699187952,{"_creationTime":3917,"_id":3918,"community":3919,"display":3920,"identity":3922,"providers":3923,"relations":3933,"tags":3934,"workflow":3935},1778675145461.8716,"k173knhqazsd87a0kmz3jp3tmn86nty4",{"reviewCount":8},{"description":2525,"installMethods":3921,"name":2526,"sourceUrl":14},{"claudeCode":12},{"basePath":2524,"githubOwner":244,"githubRepo":245,"locale":18,"slug":2526,"type":247},{"evaluate":3924,"extract":3932},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3925,"tags":3926,"targetMarket":217,"tier":218},98,[213,3878,3927,3928,212,3929,3930,3931],"classification","regression","anomaly-detection","clustering","scikit-learn",{"commitSha":253},{"repoId":255},[3929,3927,3930,212,3878,3928,3931,213],{"evaluatedAt":3936,"extractAt":3855,"updatedAt":3936},1778678143254,{"_creationTime":3938,"_id":3939,"community":3940,"display":3941,"identity":3947,"providers":3951,"relations":3961,"tags":3964,"workflow":3965},1778695548458.3625,"k17d4591dpyfqfybnac81wp9y586nh7n",{"reviewCount":8},{"description":3942,"installMethods":3943,"name":3945,"sourceUrl":3946},"Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.\n",{"claudeCode":3944},"pjt222/agent-almanac","forecast-operational-metrics","https://github.com/pjt222/agent-almanac",{"basePath":3948,"githubOwner":3949,"githubRepo":3950,"locale":18,"slug":3945,"type":247},"skills/forecast-operational-metrics","pjt222","agent-almanac",{"evaluate":3952,"extract":3960},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3953,"tags":3954,"targetMarket":217,"tier":218},95,[212,213,3955,3956,3957,3958,3959],"prophet","statsmodels","capacity-planning","grafana","mlops",{"commitSha":253},{"parentExtensionId":3962,"repoId":3963},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[3957,212,3958,3959,3955,3956,213],{"evaluatedAt":3966,"extractAt":3967,"updatedAt":3966},1778698282903,1778695548458,{"_creationTime":3969,"_id":3970,"community":3971,"display":3972,"identity":3976,"providers":3979,"relations":3985,"tags":3986,"workflow":3987},1778691799740.4673,"k178b4tn4gxjqbpqfzkces5qm186m0z3",{"reviewCount":8},{"description":3973,"installMethods":3974,"name":3975,"sourceUrl":3866},"This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.",{"claudeCode":3865},"Aeon Time Series Machine Learning",{"basePath":3977,"githubOwner":3869,"githubRepo":3870,"locale":18,"slug":3978,"type":247},"scientific-skills/aeon","aeon",{"evaluate":3980,"extract":3983},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3953,"tags":3981,"targetMarket":217,"tier":218},[213,3878,212,3927,3928,215,3982],"data-analysis",{"commitSha":253,"license":3984},"BSD-3-Clause",{"repoId":3881},[3927,3982,212,3878,215,3928,213],{"evaluatedAt":3988,"extractAt":3885,"updatedAt":3988},1778691874025,{"_creationTime":3990,"_id":3991,"community":3992,"display":3993,"identity":3997,"providers":4000,"relations":4006,"tags":4007,"workflow":4008},1778691799740.4958,"k17f4newyw03c3a37jjmvy576s86mgrc",{"reviewCount":8},{"description":3994,"installMethods":3995,"name":3996,"sourceUrl":3866},"Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.",{"claudeCode":3865},"SHAP Model Interpretability",{"basePath":3998,"githubOwner":3869,"githubRepo":3870,"locale":18,"slug":3999,"type":247},"scientific-skills/shap","shap",{"evaluate":4001,"extract":4005},{"promptVersionExtension":205,"promptVersionScoring":206,"score":3874,"tags":4002,"targetMarket":217,"tier":218},[3878,4003,4004,3999,216,215],"explainable-ai","model-interpretability",{"commitSha":253,"license":238},{"repoId":3881},[216,4003,3878,4004,215,3999],{"evaluatedAt":4009,"extractAt":3885,"updatedAt":4009},1778694453287]