[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-huggingface-paper-publisher-de":3,"guides-for-huggingface-huggingface-paper-publisher":736,"similar-k17fbzfkb9jcq3hk2k6f27d6an86ne59-de":737},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":244,"isFallback":241,"parentExtension":248,"providers":283,"relations":287,"repo":288,"tags":734,"workflow":735},1778690773482.4883,"k17fbzfkb9jcq3hk2k6f27d6an86ne59",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.",{"claudeCode":12},"huggingface/skills","huggingface-paper-publisher","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":225,"workflow":242},1778691354790.8604,"kn7apnvx2q7k4ggzjjh3p18bdd86n1vd","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":218,"tier":219,"useCases":220},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,116,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of publishing and managing research papers on Hugging Face Hub, including specific actions like creating pages, linking artifacts, and generating articles.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers a comprehensive workflow for paper management on Hugging Face Hub, integrating with arXiv, models, and datasets, which goes beyond basic LLM capabilities.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides a complete lifecycle for paper management, from creation and linking to authorship claiming, with runnable scripts and clear usage instructions.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses solely on managing research papers and their metadata on Hugging Face Hub, without extending into unrelated domains.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities of publishing, managing, and linking research papers on Hugging Face Hub.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes specific, well-defined tools like 'index', 'link', 'create', etc., avoiding general-purpose command execution.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","All script parameters are documented in the SKILL.md, usage examples, and quick reference guides, including necessary environment variables like HF_TOKEN.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names like 'index', 'link', 'create', and 'claim' are descriptive and align with their functionality within the paper management domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The script parameters are specific and required for their intended tasks, and outputs are structured JSON or clear text messages, avoiding unnecessary diagnostic dumps.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the Apache-2.0 license, as indicated by the bundled LICENSE file, which is a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The latest commit was on 2026-05-12, which is within the last 12 months.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","Dependencies are declared with specific versions in the script header, and the use of 'uv run' suggests proper environment management.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The script correctly uses environment variables (HF_TOKEN) and does not echo resolved secrets to stdout or debug logs.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The script sanitizes inputs and does not load external code or instructions from untrusted sources.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill does not fetch external code or data at runtime; all dependencies are declared and managed via 'uv run'.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The script operates on specific files (README.md) and uses relative paths within the context of Hugging Face Hub operations, respecting isolation.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached processes or deny-retry loops were found in the script.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","No instructions to exfiltrate confidential data were found; outbound calls are limited to Hugging Face Hub APIs with necessary authentication.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled markdown files are free of hidden steering tricks, control characters, or unusual formatting.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The Python script is plain, readable source code and not obfuscated, base64-encoded, or dynamically fetched.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill operates on Hugging Face Hub repositories and local files in a standard way, with clear instructions to 'cd' to the directory if needed.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","Open issues: 4, Closed issues (90d): 6. The closure rate is approximately 60%, indicating good maintainer engagement.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The skill declares version 1.0.0 in its frontmatter and uses specific dependencies, indicating a clear versioning strategy.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The script includes input validation for arXiv IDs and repository types, and sanitizes text for markdown and YAML outputs.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","Operations like updating READMEs are guarded by the necessary HF_TOKEN and clear commit messages; no silent destructive actions are performed.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The script includes error handling for API requests, file operations, and input validation, returning structured error messages.",{"category":110,"check":114,"severity":24,"summary":115},"Logging","The script provides clear print statements indicating the progress and outcome of operations, serving as basic audit logging.",{"category":117,"check":118,"severity":119,"summary":120},"Compliance","GDPR","not_applicable","The skill does not operate on personal data; it interacts with Hugging Face Hub metadata and public arXiv information.",{"category":117,"check":122,"severity":24,"summary":123},"Target market","The skill operates globally on Hugging Face Hub and arXiv, with no specific geographic or legal jurisdictional limitations.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The script uses standard Python libraries and `uv run` for dependency management, ensuring compatibility across POSIX-compliant systems.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README file clearly explains the skill's purpose, installation, and usage across different agent platforms.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The skill exposes a reasonable number of tools (index, link, create, check, info, citation, search), fitting within the recommended range.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The tool names (e.g., 'index', 'link', 'create') are distinct and cover different aspects of paper management, avoiding redundancy.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All features described in the README and SKILL.md (paper indexing, linking, creation, authorship) have corresponding implementations in the scripts.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","Detailed installation and usage instructions are provided for multiple agent platforms (Claude Code, Codex, Gemini CLI, Cursor) in the README and SKILL.md, including copy-paste examples.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","Errors related to invalid IDs, missing tokens, or not-found papers provide clear explanations and suggest remediation steps.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","Dependencies are declared with specific versions (e.g., `huggingface_hub>=0.26.0`) in the script header, and `uv run` implies lockfile usage for reproducible environments.",{"category":33,"check":151,"severity":119,"summary":152},"Dry-run preview","The skill's primary operations involve updating remote repositories (README.md), which inherently have a preview/commit step via the Hugging Face API and are not state-changing in a way that requires a dry-run flag within the script itself.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The script includes timeouts for network requests and handles potential errors gracefully, and operations like linking are idempotent due to Hugging Face API's nature.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The script does not emit any telemetry; all operations are user-initiated via CLI commands.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The skill's purpose is precisely defined as managing research papers on Hugging Face Hub, with clear actions (index, link, create) and target artifacts (papers, models, datasets).",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The SKILL.md frontmatter is concise and effectively summarizes the skill's core capability for precise routing.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is well-structured, uses progressive disclosure via examples and quick references, and stays within reasonable length.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Detailed workflows, examples, and references are provided in separate files (examples/, references/) linked from the main SKILL.md, facilitating progressive disclosure.",{"category":170,"check":174,"severity":119,"summary":175},"Forked exploration","This skill does not involve deep exploration or code review that would necessitate a forked context.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","Comprehensive, end-to-end examples are provided for all major capabilities, demonstrating input, invocation, and expected outcomes.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md and script error handling document failure modes like paper not found, permission denied, and invalid YAML, with suggested recovery steps.",{"category":110,"check":183,"severity":119,"summary":184},"Tool Fallback","This skill does not rely on an external MCP server; it uses Hugging Face Hub APIs directly and Python scripts.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The script includes checks for valid arXiv IDs and repository existence before performing actions, and reports errors clearly.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill operates standalone and does not implicitly rely on other skills; dependencies are managed via standard Python package management.",1778691354670,"This skill provides tools to index, link, and manage research papers on Hugging Face Hub. It can create paper pages, link papers to models and datasets, handle authorship claims, and generate markdown-based research articles using various templates. It relies on Python scripts and Hugging Face Hub APIs.",[195,196,197,198,199],"Index papers from arXiv to Hugging Face Hub","Link papers to model and dataset cards with YAML metadata","Generate professional markdown-based research articles from templates","Manage paper authorship and visibility on Hugging Face","Generate citations in BibTeX format",[201,202,203,204],"Submitting papers directly to arXiv (external process).","Managing academic journal submissions or peer review.","Performing complex scientific writing or editing beyond template generation.","Hosting or running ML models directly.","3.0.0","4.4.0","Streamline the process of publishing, managing, and linking research papers on Hugging Face Hub, making research discoverable and connected to relevant models and datasets.","The skill is well-documented, offers a comprehensive set of tools for paper management on Hugging Face Hub, and has a robust error handling mechanism. Dependencies are managed properly, and security checks pass.",97,"A high-quality skill for managing research papers on Hugging Face Hub, with excellent documentation and functionality.",[212,213,214,215,216,217],"huggingface","research","publishing","arxiv","documentation","workflow","global","verified",[221,222,223,224],"When you need to publish a new research paper on Hugging Face.","When you want to link your existing research papers to your models or datasets.","When you need to generate a research article template for a new paper.","When you need to manage your authored papers on Hugging Face.",{"codeQuality":226,"collectedAt":228,"documentation":229,"maintenance":232,"security":238,"testCoverage":240},{"hasLockfile":227},false,1778691335914,{"descriptionLength":230,"readmeSize":231},204,9821,{"closedIssues90d":233,"forks":234,"hasChangelog":227,"openIssues90d":235,"pushedAt":236,"stars":237},6,663,4,1778593131000,10482,{"hasNpmPackage":227,"license":239,"smitheryVerified":227},"Apache-2.0",{"hasCi":241,"hasTests":227},true,{"updatedAt":243},1778691354790,{"basePath":245,"githubOwner":212,"githubRepo":246,"locale":18,"slug":13,"type":247},"skills/huggingface-paper-publisher","skills","skill",{"_creationTime":249,"_id":250,"community":251,"display":252,"identity":257,"parentExtension":260,"providers":261,"relations":277,"tags":279,"workflow":280},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":253,"installMethods":254,"name":256,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":255},"huggingface-skills","Hugging Face Skills",{"basePath":258,"githubOwner":212,"githubRepo":246,"locale":18,"slug":246,"type":259},"","plugin",null,{"evaluate":262,"extract":272},{"promptVersionExtension":205,"promptVersionScoring":206,"score":263,"tags":264,"targetMarket":218,"tier":219},98,[212,265,266,267,268,269,270,271],"ai","ml","datasets","models","training","cli","python",{"commitSha":273,"license":239,"plugin":274},"HEAD",{"mcpCount":8,"provider":275,"skillCount":276},"classify",14,{"repoId":278},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[265,270,267,212,266,268,271,269],{"evaluatedAt":281,"extractAt":282,"updatedAt":281},1778691185872,1778690773482,{"evaluate":284,"extract":286},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":285,"targetMarket":218,"tier":219},[212,213,214,215,216,217],{"commitSha":273},{"parentExtensionId":250,"repoId":278},{"_creationTime":289,"_id":278,"identity":290,"providers":291,"workflow":730},1778689536128.5474,{"githubOwner":212,"githubRepo":246,"sourceUrl":14},{"classify":292,"discover":723,"github":726},{"commitSha":273,"extensions":293},[294,308,317,325,330,338,346,354,362,370,378,386,394,402,410,418,461,469,475,481,498,504,511,553,564,583,589,609,621,645,703],{"basePath":258,"description":253,"displayName":255,"installMethods":295,"rationale":296,"selectedPaths":297,"source":306,"sourceLanguage":18,"type":307},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[298,301,303],{"path":299,"priority":300},".claude-plugin/marketplace.json","mandatory",{"path":302,"priority":300},"README.md",{"path":304,"priority":305},"LICENSE","high","rule","marketplace",{"basePath":309,"description":310,"displayName":311,"installMethods":312,"rationale":313,"selectedPaths":314,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-llm-trainer","Train or fine-tune language models using TRL on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes hardware selection, cost estimation, Trackio monitoring, and Hub persistence.","huggingface-llm-trainer",{"claudeCode":311},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[315],{"path":316,"priority":305},"SKILL.md",{"basePath":318,"description":319,"displayName":320,"installMethods":321,"rationale":322,"selectedPaths":323,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-local-models","Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.","huggingface-local-models",{"claudeCode":320},"inline plugin source from marketplace.json at skills/huggingface-local-models",[324],{"path":316,"priority":305},{"basePath":245,"description":10,"displayName":13,"installMethods":326,"rationale":327,"selectedPaths":328,"source":306,"sourceLanguage":18,"type":259},{"claudeCode":13},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[329],{"path":316,"priority":305},{"basePath":331,"description":332,"displayName":333,"installMethods":334,"rationale":335,"selectedPaths":336,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-papers","Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata like authors, linked models, datasets, Spaces, and media URLs when needed.","huggingface-papers",{"claudeCode":333},"inline plugin source from marketplace.json at skills/huggingface-papers",[337],{"path":316,"priority":305},{"basePath":339,"description":340,"displayName":341,"installMethods":342,"rationale":343,"selectedPaths":344,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-community-evals","Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom evaluations with vLLM/lighteval.","huggingface-community-evals",{"claudeCode":341},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[345],{"path":316,"priority":305},{"basePath":347,"description":348,"displayName":349,"installMethods":350,"rationale":351,"selectedPaths":352,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-best","Find the best AI model for any task by querying Hugging Face leaderboards and benchmarks. Recommends top models based on task type, hardware constraints, and benchmark scores.","huggingface-best",{"claudeCode":349},"inline plugin source from marketplace.json at skills/huggingface-best",[353],{"path":316,"priority":305},{"basePath":355,"description":356,"displayName":357,"installMethods":358,"rationale":359,"selectedPaths":360,"source":306,"sourceLanguage":18,"type":259},"skills/hf-cli","Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.","hf-cli",{"claudeCode":357},"inline plugin source from marketplace.json at skills/hf-cli",[361],{"path":316,"priority":305},{"basePath":363,"description":364,"displayName":365,"installMethods":366,"rationale":367,"selectedPaths":368,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-trackio","Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces.","huggingface-trackio",{"claudeCode":365},"inline plugin source from marketplace.json at skills/huggingface-trackio",[369],{"path":316,"priority":305},{"basePath":371,"description":372,"displayName":373,"installMethods":374,"rationale":375,"selectedPaths":376,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-datasets","Explore, query, and extract data from any Hugging Face dataset using the Dataset Viewer REST API and npx tooling. Zero Python dependencies — covers split/config discovery, row pagination, text search, filtering, SQL via parquetlens, and dataset upload via CLI.","huggingface-datasets",{"claudeCode":373},"inline plugin source from marketplace.json at skills/huggingface-datasets",[377],{"path":316,"priority":305},{"basePath":379,"description":380,"displayName":381,"installMethods":382,"rationale":383,"selectedPaths":384,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-tool-builder","Build reusable scripts for Hugging Face Hub and API workflows. Useful for chaining API calls, enriching Hub metadata, or automating repeated tasks.","huggingface-tool-builder",{"claudeCode":381},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[385],{"path":316,"priority":305},{"basePath":387,"description":388,"displayName":389,"installMethods":390,"rationale":391,"selectedPaths":392,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-gradio","Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.","huggingface-gradio",{"claudeCode":389},"inline plugin source from marketplace.json at skills/huggingface-gradio",[393],{"path":316,"priority":305},{"basePath":395,"description":396,"displayName":397,"installMethods":398,"rationale":399,"selectedPaths":400,"source":306,"sourceLanguage":18,"type":259},"skills/transformers-js","Run state-of-the-art machine learning models directly in JavaScript/TypeScript for NLP, computer vision, audio processing, and multimodal tasks. Works in Node.js and browsers with WebGPU/WASM using Hugging Face models.","transformers-js",{"claudeCode":397},"inline plugin source from marketplace.json at skills/transformers-js",[401],{"path":316,"priority":305},{"basePath":403,"description":404,"displayName":405,"installMethods":406,"rationale":407,"selectedPaths":408,"source":306,"sourceLanguage":18,"type":259},"skills/huggingface-vision-trainer","Train and fine-tune object detection models (RTDETRv2, YOLOS, DETR and others) and image classification models (timm and transformers models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3) using Transformers Trainer API on Hugging Face Jobs infrastructure or locally. Includes COCO dataset format support, Albumentations augmentation, mAP/mAR metrics, trackio tracking, hardware selection, and Hub persistence.","huggingface-vision-trainer",{"claudeCode":405},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[409],{"path":316,"priority":305},{"basePath":411,"description":412,"displayName":413,"installMethods":414,"rationale":415,"selectedPaths":416,"source":306,"sourceLanguage":18,"type":259},"skills/train-sentence-transformers","Train or fine-tune sentence-transformers models across all three architectures: SentenceTransformer (bi-encoder embeddings), CrossEncoder (rerankers), and SparseEncoder (SPLADE). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing.","train-sentence-transformers",{"claudeCode":413},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[417],{"path":316,"priority":305},{"basePath":258,"description":253,"displayName":255,"installMethods":419,"license":239,"rationale":420,"selectedPaths":421,"source":306,"sourceLanguage":18,"type":259},{"claudeCode":255},"plugin manifest at .claude-plugin/plugin.json",[422,424,425,426,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459],{"path":423,"priority":300},".claude-plugin/plugin.json",{"path":302,"priority":300},{"path":304,"priority":305},{"path":427,"priority":428},"skills/hf-cli/SKILL.md","medium",{"path":430,"priority":428},"skills/huggingface-best/SKILL.md",{"path":432,"priority":428},"skills/huggingface-community-evals/SKILL.md",{"path":434,"priority":428},"skills/huggingface-datasets/SKILL.md",{"path":436,"priority":428},"skills/huggingface-gradio/SKILL.md",{"path":438,"priority":428},"skills/huggingface-llm-trainer/SKILL.md",{"path":440,"priority":428},"skills/huggingface-local-models/SKILL.md",{"path":442,"priority":428},"skills/huggingface-paper-publisher/SKILL.md",{"path":444,"priority":428},"skills/huggingface-papers/SKILL.md",{"path":446,"priority":428},"skills/huggingface-tool-builder/SKILL.md",{"path":448,"priority":428},"skills/huggingface-trackio/SKILL.md",{"path":450,"priority":428},"skills/huggingface-vision-trainer/SKILL.md",{"path":452,"priority":428},"skills/train-sentence-transformers/SKILL.md",{"path":454,"priority":428},"skills/transformers-js/SKILL.md",{"path":456,"priority":300},".mcp.json",{"path":458,"priority":305},"agents/AGENTS.md",{"path":460,"priority":305},".cursor-plugin/plugin.json",{"basePath":462,"description":463,"displayName":464,"installMethods":465,"rationale":466,"selectedPaths":467,"source":306,"sourceLanguage":18,"type":247},"hf-mcp/skills/hf-mcp","Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.","hf-mcp",{"claudeCode":12},"SKILL.md frontmatter at hf-mcp/skills/hf-mcp/SKILL.md",[468],{"path":316,"priority":300},{"basePath":355,"description":470,"displayName":357,"installMethods":471,"rationale":472,"selectedPaths":473,"source":306,"sourceLanguage":18,"type":247},"Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.",{"claudeCode":12},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[474],{"path":316,"priority":300},{"basePath":347,"description":476,"displayName":349,"installMethods":477,"rationale":478,"selectedPaths":479,"source":306,"sourceLanguage":18,"type":247},"Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my laptop/machine/device\", \"recommend a model for\", \"what LLM should I use for\", \"compare models for\", \"what's state of the art for\", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.\n",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-best/SKILL.md",[480],{"path":316,"priority":300},{"basePath":339,"description":482,"displayName":341,"installMethods":483,"rationale":484,"selectedPaths":485,"source":306,"sourceLanguage":18,"type":247},"Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-community-evals/SKILL.md",[486,487,490,492,494,496],{"path":316,"priority":300},{"path":488,"priority":489},"examples/.env.example","low",{"path":491,"priority":489},"examples/USAGE_EXAMPLES.md",{"path":493,"priority":489},"scripts/inspect_eval_uv.py",{"path":495,"priority":489},"scripts/inspect_vllm_uv.py",{"path":497,"priority":489},"scripts/lighteval_vllm_uv.py",{"basePath":371,"description":499,"displayName":373,"installMethods":500,"rationale":501,"selectedPaths":502,"source":306,"sourceLanguage":18,"type":247},"Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.\r",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-datasets/SKILL.md",[503],{"path":316,"priority":300},{"basePath":387,"description":388,"displayName":389,"installMethods":505,"rationale":506,"selectedPaths":507,"source":306,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[508,509],{"path":316,"priority":300},{"path":510,"priority":428},"examples.md",{"basePath":309,"description":512,"displayName":311,"installMethods":513,"rationale":514,"selectedPaths":515,"source":306,"sourceLanguage":18,"type":247},"Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-llm-trainer/SKILL.md",[516,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551],{"path":316,"priority":300},{"path":518,"priority":428},"references/gguf_conversion.md",{"path":520,"priority":428},"references/hardware_guide.md",{"path":522,"priority":428},"references/hub_saving.md",{"path":524,"priority":428},"references/local_training_macos.md",{"path":526,"priority":428},"references/reliability_principles.md",{"path":528,"priority":428},"references/trackio_guide.md",{"path":530,"priority":428},"references/training_methods.md",{"path":532,"priority":428},"references/training_patterns.md",{"path":534,"priority":428},"references/troubleshooting.md",{"path":536,"priority":428},"references/unsloth.md",{"path":538,"priority":489},"scripts/convert_to_gguf.py",{"path":540,"priority":489},"scripts/dataset_inspector.py",{"path":542,"priority":489},"scripts/estimate_cost.py",{"path":544,"priority":489},"scripts/hf_benchmarks.py",{"path":546,"priority":489},"scripts/train_dpo_example.py",{"path":548,"priority":489},"scripts/train_grpo_example.py",{"path":550,"priority":489},"scripts/train_sft_example.py",{"path":552,"priority":489},"scripts/unsloth_sft_example.py",{"basePath":318,"description":319,"displayName":320,"installMethods":554,"rationale":555,"selectedPaths":556,"source":306,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[557,558,560,562],{"path":316,"priority":300},{"path":559,"priority":428},"references/hardware.md",{"path":561,"priority":428},"references/hub-discovery.md",{"path":563,"priority":428},"references/quantization.md",{"basePath":245,"description":10,"displayName":13,"installMethods":565,"rationale":566,"selectedPaths":567,"source":306,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[568,569,571,573,575,577,579,581],{"path":316,"priority":300},{"path":570,"priority":489},"examples/example_usage.md",{"path":572,"priority":428},"references/quick_reference.md",{"path":574,"priority":489},"scripts/paper_manager.py",{"path":576,"priority":489},"templates/arxiv.md",{"path":578,"priority":489},"templates/ml-report.md",{"path":580,"priority":489},"templates/modern.md",{"path":582,"priority":489},"templates/standard.md",{"basePath":331,"description":584,"displayName":333,"installMethods":585,"rationale":586,"selectedPaths":587,"source":306,"sourceLanguage":18,"type":247},"Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-papers/SKILL.md",[588],{"path":316,"priority":300},{"basePath":379,"description":590,"displayName":381,"installMethods":591,"rationale":592,"selectedPaths":593,"source":306,"sourceLanguage":18,"type":247},"Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-tool-builder/SKILL.md",[594,595,597,599,601,603,605,607],{"path":316,"priority":300},{"path":596,"priority":428},"references/baseline_hf_api.py",{"path":598,"priority":428},"references/baseline_hf_api.sh",{"path":600,"priority":428},"references/baseline_hf_api.tsx",{"path":602,"priority":428},"references/find_models_by_paper.sh",{"path":604,"priority":428},"references/hf_enrich_models.sh",{"path":606,"priority":428},"references/hf_model_card_frontmatter.sh",{"path":608,"priority":428},"references/hf_model_papers_auth.sh",{"basePath":363,"description":610,"displayName":365,"installMethods":611,"rationale":612,"selectedPaths":613,"source":306,"sourceLanguage":18,"type":247},"Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-trackio/SKILL.md",[614,615,617,619],{"path":316,"priority":300},{"path":616,"priority":428},"references/alerts.md",{"path":618,"priority":428},"references/logging_metrics.md",{"path":620,"priority":428},"references/retrieving_metrics.md",{"basePath":403,"description":622,"displayName":405,"installMethods":623,"rationale":624,"selectedPaths":625,"source":306,"sourceLanguage":18,"type":247},"Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-vision-trainer/SKILL.md",[626,627,629,630,632,634,635,637,638,639,641,643],{"path":316,"priority":300},{"path":628,"priority":428},"references/finetune_sam2_trainer.md",{"path":522,"priority":428},{"path":631,"priority":428},"references/image_classification_training_notebook.md",{"path":633,"priority":428},"references/object_detection_training_notebook.md",{"path":526,"priority":428},{"path":636,"priority":428},"references/timm_trainer.md",{"path":540,"priority":489},{"path":542,"priority":489},{"path":640,"priority":489},"scripts/image_classification_training.py",{"path":642,"priority":489},"scripts/object_detection_training.py",{"path":644,"priority":489},"scripts/sam_segmentation_training.py",{"basePath":411,"description":646,"displayName":413,"installMethods":647,"rationale":648,"selectedPaths":649,"source":306,"sourceLanguage":18,"type":247},"Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.",{"claudeCode":12},"SKILL.md frontmatter at skills/train-sentence-transformers/SKILL.md",[650,651,653,655,657,659,661,662,664,666,668,670,672,674,676,677,679,681,683,685,687,689,691,693,695,697,699,701],{"path":316,"priority":300},{"path":652,"priority":428},"references/base_model_selection.md",{"path":654,"priority":428},"references/dataset_formats.md",{"path":656,"priority":428},"references/evaluators_cross_encoder.md",{"path":658,"priority":428},"references/evaluators_sentence_transformer.md",{"path":660,"priority":428},"references/evaluators_sparse_encoder.md",{"path":520,"priority":428},{"path":663,"priority":428},"references/hf_jobs_execution.md",{"path":665,"priority":428},"references/losses_cross_encoder.md",{"path":667,"priority":428},"references/losses_sentence_transformer.md",{"path":669,"priority":428},"references/losses_sparse_encoder.md",{"path":671,"priority":428},"references/model_architectures.md",{"path":673,"priority":428},"references/prompts_and_instructions.md",{"path":675,"priority":428},"references/training_args.md",{"path":534,"priority":428},{"path":678,"priority":489},"scripts/mine_hard_negatives.py",{"path":680,"priority":489},"scripts/train_cross_encoder_distillation_example.py",{"path":682,"priority":489},"scripts/train_cross_encoder_example.py",{"path":684,"priority":489},"scripts/train_cross_encoder_listwise_example.py",{"path":686,"priority":489},"scripts/train_sentence_transformer_distillation_example.py",{"path":688,"priority":489},"scripts/train_sentence_transformer_example.py",{"path":690,"priority":489},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":692,"priority":489},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":694,"priority":489},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":696,"priority":489},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":698,"priority":489},"scripts/train_sentence_transformer_with_lora_example.py",{"path":700,"priority":489},"scripts/train_sparse_encoder_distillation_example.py",{"path":702,"priority":489},"scripts/train_sparse_encoder_example.py",{"basePath":395,"description":704,"displayName":397,"installMethods":705,"rationale":706,"selectedPaths":707,"source":306,"sourceLanguage":18,"type":247},"Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.",{"claudeCode":12},"SKILL.md frontmatter at skills/transformers-js/SKILL.md",[708,709,711,713,715,717,719,721],{"path":316,"priority":300},{"path":710,"priority":428},"references/CACHE.md",{"path":712,"priority":428},"references/CONFIGURATION.md",{"path":714,"priority":428},"references/EXAMPLES.md",{"path":716,"priority":428},"references/MODEL_ARCHITECTURES.md",{"path":718,"priority":428},"references/MODEL_REGISTRY.md",{"path":720,"priority":428},"references/PIPELINE_OPTIONS.md",{"path":722,"priority":428},"references/TEXT_GENERATION.md",{"sources":724},[725],"manual",{"closedIssues90d":233,"description":727,"forks":234,"homepage":728,"license":239,"openIssues90d":235,"pushedAt":236,"readmeSize":231,"stars":237,"topics":729},"Give your agents the power of the Hugging Face ecosystem","https://huggingface.co",[],{"classifiedAt":731,"discoverAt":732,"extractAt":733,"githubAt":733,"updatedAt":731},1778690772996,1778689536128,1778690770714,[215,216,212,214,213,217],{"evaluatedAt":243,"extractAt":282,"updatedAt":243},[],[738,767,794,818,836,866],{"_creationTime":739,"_id":740,"community":741,"display":742,"identity":748,"providers":752,"relations":760,"tags":763,"workflow":764},1778699234184.6135,"k175frmf44tn80mcd6gvw1c1th86ngq9",{"reviewCount":8},{"description":743,"installMethods":744,"name":746,"sourceUrl":747},"Invoke parallel document-specialist agents for external web searches and documentation lookup",{"claudeCode":745},"Yeachan-Heo/oh-my-claudecode","external-context","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":749,"githubOwner":750,"githubRepo":751,"locale":18,"slug":746,"type":247},"skills/external-context","Yeachan-Heo","oh-my-claudecode",{"evaluate":753,"extract":759},{"promptVersionExtension":205,"promptVersionScoring":206,"score":754,"tags":755,"targetMarket":218,"tier":219},100,[756,216,213,757,758],"search","information-retrieval","multi-agent",{"commitSha":273},{"parentExtensionId":761,"repoId":762},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[216,757,758,213,756],{"evaluatedAt":765,"extractAt":766,"updatedAt":765},1778699449790,1778699234184,{"_creationTime":768,"_id":769,"community":770,"display":771,"identity":777,"providers":781,"relations":788,"tags":790,"workflow":791},1778696505500.006,"k1754f7285hvja3svvh212kj8586maxr",{"reviewCount":8},{"description":772,"installMethods":773,"name":775,"sourceUrl":776},"Checklist and automation guide for adding a new skill to the OPC Skills project. Ensures all required files, metadata, logos, and listings are created before release. Use when adding a new skill, publishing a skill, or preparing a skill for release.",{"claudeCode":774},"ReScienceLab/opc-skills","add-new-opc-skill","https://github.com/ReScienceLab/opc-skills",{"basePath":778,"githubOwner":779,"githubRepo":780,"locale":18,"slug":775,"type":247},".factory/skills/add-new-opc-skill","ReScienceLab","opc-skills",{"evaluate":782,"extract":787},{"promptVersionExtension":205,"promptVersionScoring":206,"score":754,"tags":783,"targetMarket":218,"tier":219},[216,784,785,786,217],"automation","development","checklist",{"commitSha":273},{"repoId":789},"kd7fj56h5kejcgm6hcjmzn79xd86m7wa",[784,786,785,216,217],{"evaluatedAt":792,"extractAt":793,"updatedAt":792},1778696744286,1778696505500,{"_creationTime":795,"_id":796,"community":797,"display":798,"identity":804,"providers":807,"relations":812,"tags":814,"workflow":815},1778694578248.1042,"k17fdepncm15jzpekss5e8a0m986n6xd",{"reviewCount":8},{"description":799,"installMethods":800,"name":802,"sourceUrl":803},"Authoritative reference for how docs in this repo (and 5 other Netdata-org repos) become published pages on `learn.netdata.cloud`. Covers the `\u003Crepo>/docs/.map/map.yaml` source-of-truth (the actual lever -- filesystem path is irrelevant for routing), the live `ingest/ingest.py` orchestrator in the learn repo (NOT the legacy `ingest.js`), frontmatter injection, slug rules, sidebar autogeneration, MDX escape rules, versioning, the 4-mechanism redirect stack, the 6 source repositories, the every-3-hours CI ingest, Netlify deploy, and the `part_of_learn=True` opt-in for files hand-authored in the learn repo. Use when adding/moving/renaming/deleting a docs page; when a page on Learn looks wrong; when wondering whether to edit a doc here or in the learn repo; when reading `ingest.py`, `sidebars.js`, `docusaurus.config.js`, `static.toml`, `LegacyLearnCorrelateLinksWithGHURLs.json`, `netlify.toml`, the `\u003C!--startmeta` blocks in `.mdx` files, or the workflows `ingest.yml` and `daily-learn-link-check.yml`.",{"claudeCode":801},"netdata/netdata","learn-site-structure","https://github.com/netdata/netdata",{"basePath":805,"githubOwner":806,"githubRepo":806,"locale":18,"slug":802,"type":247},".agents/skills/learn-site-structure","netdata",{"evaluate":808,"extract":811},{"promptVersionExtension":205,"promptVersionScoring":206,"score":754,"tags":809,"targetMarket":218,"tier":219},[216,214,217,806,810],"ci-cd",{"commitSha":273},{"repoId":813},"kd70yp91ybn40a638h3hzz6nbd86m2cw",[810,216,806,214,217],{"evaluatedAt":816,"extractAt":817,"updatedAt":816},1778694681982,1778694578248,{"_creationTime":819,"_id":820,"community":821,"display":822,"identity":824,"providers":825,"relations":832,"tags":833,"workflow":834},1778690773482.4885,"k177asm0v1bhrzc0gq52a936z586memq",{"reviewCount":8},{"description":584,"installMethods":823,"name":333,"sourceUrl":14},{"claudeCode":12},{"basePath":331,"githubOwner":212,"githubRepo":246,"locale":18,"slug":333,"type":247},{"evaluate":826,"extract":831},{"promptVersionExtension":205,"promptVersionScoring":206,"score":827,"tags":828,"targetMarket":218,"tier":219},99,[212,829,213,830,215],"papers","api",{"commitSha":273},{"parentExtensionId":250,"repoId":278},[830,215,212,829,213],{"evaluatedAt":835,"extractAt":282,"updatedAt":835},1778691369571,{"_creationTime":837,"_id":838,"community":839,"display":840,"identity":846,"providers":850,"relations":859,"tags":862,"workflow":863},1778695548458.4048,"k17e5nn93syzxrybh3he9fz5eh86nbme",{"reviewCount":8},{"description":841,"installMethods":842,"name":844,"sourceUrl":845},"Guide a person in becoming a better teacher and explainer. AI coaches content structuring, audience calibration, explanation clarity, Socratic questioning technique, feedback interpretation, and reflective practice for technical presentations, documentation, and mentoring. Use when a person needs to present technical content and wants preparation coaching, wants to write better documentation or tutorials, struggles to explain concepts across expertise levels, is mentoring a colleague, or is preparing for a talk or knowledge-sharing session.\n",{"claudeCode":843},"pjt222/agent-almanac","teach-guidance","https://github.com/pjt222/agent-almanac",{"basePath":847,"githubOwner":848,"githubRepo":849,"locale":18,"slug":844,"type":247},"skills/teach-guidance","pjt222","agent-almanac",{"evaluate":851,"extract":858},{"promptVersionExtension":205,"promptVersionScoring":206,"score":754,"tags":852,"targetMarket":218,"tier":219},[853,854,855,216,856,857],"teaching","coaching","presentation","explanation","guidance",{"commitSha":273},{"parentExtensionId":860,"repoId":861},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[854,216,856,857,855,853],{"evaluatedAt":864,"extractAt":865,"updatedAt":864},1778701952682,1778695548458,{"_creationTime":867,"_id":868,"community":869,"display":870,"identity":874,"providers":876,"relations":888,"tags":889,"workflow":890},1778695548458.385,"k17avw7n0q0zss1q5kna5zvjzx86mdvr",{"reviewCount":8},{"description":871,"installMethods":872,"name":873,"sourceUrl":845},"Prepare an organisation for regulatory inspection by assessing readiness against agency-specific focus areas (FDA, EMA, MHRA). Covers warning letter and 483 theme analysis, mock inspection protocols, document bundle preparation, inspection logistics, and response template creation. Use when a regulatory inspection has been announced or is anticipated, when a periodic self-assessment is due, when new systems have been implemented since the last inspection, or after a significant audit finding that may attract regulatory attention.\n",{"claudeCode":843},"prepare-inspection-readiness",{"basePath":875,"githubOwner":848,"githubRepo":849,"locale":18,"slug":873,"type":247},"skills/prepare-inspection-readiness",{"evaluate":877,"extract":887},{"promptVersionExtension":205,"promptVersionScoring":206,"score":754,"tags":878,"targetMarket":218,"tier":219},[879,880,881,882,883,884,885,216,886],"compliance","gxp","inspection","fda","ema","mhra","readiness","process-automation",{"commitSha":273},{"parentExtensionId":860,"repoId":861},[879,216,883,882,880,881,884,886,885],{"evaluatedAt":891,"extractAt":865,"updatedAt":891},1778700122939]