[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-huggingface-trackio-en":3,"guides-for-huggingface-huggingface-trackio":750,"similar-k17826t6czszbm021pkj9f9hes86nqv1-en":751},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":259,"isFallback":242,"parentExtension":264,"providers":297,"relations":301,"repo":302,"tags":748,"workflow":749},1778690773482.489,"k17826t6czszbm021pkj9f9hes86nqv1",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","Trackio","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":240,"workflow":257},1778691424326.0127,"kn76tpt0vqmwzsdms244zjz2k186nftg","en",{"checks":20,"evaluatedAt":201,"extensionSummary":202,"features":203,"nonGoals":209,"practices":213,"prerequisites":218,"promptVersionExtension":221,"promptVersionScoring":222,"purpose":223,"rationale":224,"score":225,"summary":226,"tags":227,"targetMarket":233,"tier":234,"useCases":235},[21,26,29,33,37,41,45,48,52,56,60,63,66,69,73,76,79,82,85,88,92,96,99,103,106,109,112,115,118,121,125,128,132,136,140,143,147,151,154,158,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200],{"category":22,"check":23,"severity":24,"summary":25},"Invocation","Precise Purpose","pass","The description clearly states the extension's purpose (tracking and visualizing ML training experiments) and when to use it (logging metrics, firing alerts, retrieving/analyzing metrics), naming the artifact (ML training experiments) and the user intent verbs.",{"category":22,"check":27,"severity":24,"summary":28},"Concise Frontmatter","The frontmatter is concise and self-contained, clearly summarizing the core capability and providing trigger phrases.",{"category":30,"check":31,"severity":24,"summary":32},"Documentation","Concise Body","The SKILL.md body is concise and delegates deeper material to separate reference files.",{"category":34,"check":35,"severity":24,"summary":36},"Context","Progressive Disclosure","The SKILL.md outlines the flow and links into `references/` for sub-tasks, demonstrating progressive disclosure.",{"category":34,"check":38,"severity":39,"summary":40},"Forked exploration","not_applicable","The skill is not an exploration or audit-style skill and does not explore beyond its own bundle, so `context: fork` is not applicable.",{"category":42,"check":43,"severity":24,"summary":44},"Practical Utility","Usage examples","Sufficient end-to-end examples are provided for logging and retrieval, and they plausibly produce the claimed output.",{"category":42,"check":46,"severity":24,"summary":47},"Edge cases","The skill handles edge cases, with documented failure modes (e.g., missing project/run/metric) and clear recovery steps via CLI error messages.",{"category":49,"check":50,"severity":39,"summary":51},"Code Execution","Tool Fallback","The skill uses only Claude-internal tools and does not rely on external MCP servers, making fallback logic not applicable.",{"category":53,"check":54,"severity":24,"summary":55},"Safety","Halt on unexpected state","The CLI commands validate inputs and report clear, non-zero exit codes on unexpected states like missing projects or runs.",{"category":57,"check":58,"severity":24,"summary":59},"Portability","Cross-skill coupling","The skill is self-contained and does not implicitly rely on other skills being loaded in the same session.",{"category":42,"check":61,"severity":24,"summary":62},"Problem relevance","The description clearly names the problem of tracking and visualizing ML training experiments.",{"category":42,"check":64,"severity":24,"summary":65},"Unique selling proposition","Trackio offers value beyond basic logging by providing remote synchronization to HF Spaces, real-time dashboards, structured alerts, and a CLI for analysis.",{"category":42,"check":67,"severity":24,"summary":68},"Production readiness","The skill provides a complete lifecycle for ML experiment tracking, from logging via API to retrieval and visualization via CLI and HF Spaces.",{"category":70,"check":71,"severity":24,"summary":72},"Scope","Single responsibility principle","The extension focuses solely on ML experiment tracking and visualization, with no unrelated capabilities.",{"category":70,"check":74,"severity":24,"summary":75},"Description quality","The displayed description accurately and concisely reflects the extension's capabilities.",{"category":22,"check":77,"severity":24,"summary":78},"Scoped tools","The extension exposes narrow, verb-noun specific tools (Python API functions and CLI commands) rather than a single generalist tool.",{"category":30,"check":80,"severity":24,"summary":81},"Configuration & parameter reference","All parameters for `trackio.init()`, `trackio.log()`, `trackio.alert()`, and CLI commands are documented, including defaults and usage.",{"category":70,"check":83,"severity":24,"summary":84},"Tool naming","Tool names (Python functions and CLI commands) are descriptive and follow conventions.",{"category":70,"check":86,"severity":24,"summary":87},"Minimal I/O surface","Inputs for the Python API and CLI are structured and documented, and outputs are focused on the promised metrics and alerts.",{"category":89,"check":90,"severity":24,"summary":91},"License","License usability","The extension is licensed under the Apache-2.0 license, as indicated by the bundled LICENSE file.",{"category":93,"check":94,"severity":24,"summary":95},"Maintenance","Commit recency","The last commit was on May 12, 2026, which is recent, indicating active maintenance.",{"category":93,"check":97,"severity":24,"summary":98},"Dependency Management","The extension uses standard libraries and mentions `pip install trackio` which implies standard dependency management. No explicit mention of lockfiles but the nature of the tool suggests standard Python packaging practices apply.",{"category":100,"check":101,"severity":24,"summary":102},"Security","Secret Management","No secrets are used or handled by this extension.",{"category":100,"check":104,"severity":24,"summary":105},"Injection","The skill does not load 3rd party data or files at runtime that could contain instructions, and all its content is bundled.",{"category":100,"check":107,"severity":24,"summary":108},"Transitive Supply-Chain Grenades","The extension does not fetch remote content at runtime to execute as instructions. All code and data are bundled.",{"category":100,"check":110,"severity":24,"summary":111},"Sandbox Isolation","The skill operates within its defined scope and does not attempt to modify files or paths outside its designated project folder.",{"category":100,"check":113,"severity":24,"summary":114},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were found in the scripts.",{"category":100,"check":116,"severity":24,"summary":117},"Data Exfiltration","The extension does not read or submit confidential data to a third party. Network calls are for syncing to Hugging Face Spaces.",{"category":100,"check":119,"severity":24,"summary":120},"Hidden Text Tricks","Bundled content is free of hidden-steering tricks, and descriptions use clean, printable ASCII and standard Unicode.",{"category":122,"check":123,"severity":24,"summary":124},"Hooks","Opaque code execution","The bundle includes plain, readable Python source code and no obfuscated or dynamically fetched code.",{"category":57,"check":126,"severity":24,"summary":127},"Structural Assumption","The skill makes no structural assumptions about the user's project layout outside of its own bundle.",{"category":129,"check":130,"severity":24,"summary":131},"Trust","Issues Attention","With 4 issues opened and 6 closed in the last 90 days, the closure rate is high (60%), indicating good maintainer engagement.",{"category":133,"check":134,"severity":24,"summary":135},"Versioning","Release Management","The project uses semantic versioning, as indicated by the LICENSE file and standard Python packaging practices, although a specific version number is not explicitly listed in the SKILL.md frontmatter. The install instructions do not reference `main`.",{"category":137,"check":138,"severity":24,"summary":139},"Execution","Validation","The CLI commands validate inputs and provide schema-based error messages for malformed requests.",{"category":100,"check":141,"severity":24,"summary":142},"Unguarded Destructive Operations","The skill is primarily analytical. Syncing to HF Spaces is not a destructive operation.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","Errors consistently include what failed (e.g., 'Project not found'), why (e.g., 'not found'), and a clear remediation (e.g., ensure project exists).",{"category":137,"check":148,"severity":149,"summary":150},"Pinned dependencies","warning","While `pip install trackio` is mentioned, there is no explicit lockfile or pinned interpreter declaration in the bundled scripts, which could lead to unexpected behavior with dependency updates.",{"category":70,"check":152,"severity":39,"summary":153},"Dry-run preview","The skill is primarily for logging and retrieval of metrics, not for state-changing operations that would require a dry-run mode.",{"category":155,"check":156,"severity":24,"summary":157},"Protocol","Idempotent retry & timeouts","The operations are largely idempotent or read-only. Network calls for syncing are standard and expected to handle retries gracefully.",{"category":159,"check":160,"severity":24,"summary":161},"Compliance","Telemetry opt-in","The extension does not emit telemetry by default, and any data sent is related to syncing metrics to Hugging Face Spaces, which is an opt-in user action.",{"category":22,"check":27,"severity":24,"summary":28},{"category":30,"check":31,"severity":24,"summary":32},{"category":34,"check":35,"severity":24,"summary":36},{"category":34,"check":38,"severity":39,"summary":40},{"category":42,"check":43,"severity":24,"summary":44},{"category":42,"check":46,"severity":24,"summary":47},{"category":49,"check":50,"severity":39,"summary":51},{"category":53,"check":54,"severity":24,"summary":55},{"category":57,"check":58,"severity":24,"summary":59},{"category":42,"check":61,"severity":24,"summary":62},{"category":42,"check":64,"severity":24,"summary":65},{"category":42,"check":67,"severity":24,"summary":68},{"category":70,"check":71,"severity":24,"summary":72},{"category":70,"check":74,"severity":24,"summary":75},{"category":22,"check":77,"severity":24,"summary":78},{"category":30,"check":80,"severity":24,"summary":81},{"category":70,"check":83,"severity":24,"summary":84},{"category":70,"check":86,"severity":24,"summary":87},{"category":89,"check":90,"severity":24,"summary":91},{"category":93,"check":94,"severity":24,"summary":95},{"category":93,"check":97,"severity":24,"summary":98},{"category":100,"check":101,"severity":24,"summary":102},{"category":100,"check":104,"severity":24,"summary":105},{"category":100,"check":107,"severity":24,"summary":108},{"category":100,"check":110,"severity":24,"summary":111},{"category":100,"check":113,"severity":24,"summary":114},{"category":100,"check":116,"severity":24,"summary":117},{"category":100,"check":119,"severity":24,"summary":120},{"category":122,"check":123,"severity":24,"summary":124},{"category":57,"check":126,"severity":24,"summary":127},{"category":129,"check":130,"severity":24,"summary":131},{"category":133,"check":134,"severity":24,"summary":135},{"category":137,"check":138,"severity":24,"summary":139},{"category":100,"check":141,"severity":24,"summary":142},{"category":144,"check":145,"severity":24,"summary":146},{"category":137,"check":148,"severity":149,"summary":150},{"category":70,"check":152,"severity":39,"summary":153},{"category":155,"check":156,"severity":24,"summary":157},{"category":159,"check":160,"severity":24,"summary":161},1778691422192,"Trackio is a Python library and CLI tool for logging, retrieving, and visualizing ML training experiment metrics. It supports local storage and dashboard, remote syncing to Hugging Face Spaces, and alerts via API or webhooks. It integrates with TRL and Transformers for automatic logging.",[204,205,206,207,208],"Log metrics during training via Python API","Fire alerts for training diagnostics","Retrieve and analyze logged metrics via CLI","Real-time dashboard visualization (local and HF Space)","Alerts with webhook support",[210,211,212],"Replacing core ML training frameworks","Providing hyperparameter optimization algorithms","Managing or executing ML training jobs directly",[214,215,216,217],"Experiment Tracking","MLOps","Data Logging","Alerting",[219,220],"Python 3.8+","pip or equivalent package manager","3.0.0","4.4.0","Track and visualize ML training experiments to monitor progress, diagnose issues, and iterate on model development.","The extension is well-documented and production-ready, with a single warning regarding pinned dependencies.",85,"A robust and well-documented skill for tracking ML training experiments, offering both Python API and CLI interfaces.",[228,229,230,231,232],"ml","experiment-tracking","python","cli","huggingface","global","community",[236,237,238,239],"Logging metrics during ML model training","Setting up diagnostic alerts for training runs","Retrieving and analyzing experiment metrics programmatically","Visualizing training progress in real-time on a dashboard",{"codeQuality":241,"collectedAt":243,"documentation":244,"maintenance":247,"security":253,"testCoverage":255},{"hasLockfile":242},false,1778691398860,{"descriptionLength":245,"readmeSize":246},314,9821,{"closedIssues90d":248,"forks":249,"hasChangelog":242,"openIssues90d":250,"pushedAt":251,"stars":252},6,663,4,1778593131000,10482,{"hasNpmPackage":242,"license":254,"smitheryVerified":242},"Apache-2.0",{"hasCi":256,"hasTests":242},true,{"updatedAt":258},1778691424326,{"basePath":260,"githubOwner":232,"githubRepo":261,"locale":18,"slug":262,"type":263},"skills/huggingface-trackio","skills","huggingface-trackio","skill",{"_creationTime":265,"_id":266,"community":267,"display":268,"identity":273,"parentExtension":276,"providers":277,"relations":291,"tags":293,"workflow":294},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":269,"installMethods":270,"name":272,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":271},"huggingface-skills","Hugging Face Skills",{"basePath":274,"githubOwner":232,"githubRepo":261,"locale":18,"slug":261,"type":275},"","plugin",null,{"evaluate":278,"extract":286},{"promptVersionExtension":221,"promptVersionScoring":222,"score":279,"tags":280,"targetMarket":233,"tier":285},98,[232,281,228,282,283,284,231,230],"ai","datasets","models","training","verified",{"commitSha":287,"license":254,"plugin":288},"HEAD",{"mcpCount":8,"provider":289,"skillCount":290},"classify",14,{"repoId":292},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[281,231,282,232,228,283,230,284],{"evaluatedAt":295,"extractAt":296,"updatedAt":295},1778691185872,1778690773482,{"evaluate":298,"extract":300},{"promptVersionExtension":221,"promptVersionScoring":222,"score":225,"tags":299,"targetMarket":233,"tier":234},[228,229,230,231,232],{"commitSha":287,"license":254},{"parentExtensionId":266,"repoId":292},{"_creationTime":303,"_id":292,"identity":304,"providers":305,"workflow":744},1778689536128.5474,{"githubOwner":232,"githubRepo":261,"sourceUrl":14},{"classify":306,"discover":737,"github":740},{"commitSha":287,"extensions":307},[308,322,331,339,347,355,363,371,379,385,393,401,409,417,425,433,476,484,490,496,513,519,526,568,579,598,604,624,635,659,717],{"basePath":274,"description":269,"displayName":271,"installMethods":309,"rationale":310,"selectedPaths":311,"source":320,"sourceLanguage":18,"type":321},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[312,315,317],{"path":313,"priority":314},".claude-plugin/marketplace.json","mandatory",{"path":316,"priority":314},"README.md",{"path":318,"priority":319},"LICENSE","high","rule","marketplace",{"basePath":323,"description":324,"displayName":325,"installMethods":326,"rationale":327,"selectedPaths":328,"source":320,"sourceLanguage":18,"type":275},"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":325},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[329],{"path":330,"priority":319},"SKILL.md",{"basePath":332,"description":333,"displayName":334,"installMethods":335,"rationale":336,"selectedPaths":337,"source":320,"sourceLanguage":18,"type":275},"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":334},"inline plugin source from marketplace.json at skills/huggingface-local-models",[338],{"path":330,"priority":319},{"basePath":340,"description":341,"displayName":342,"installMethods":343,"rationale":344,"selectedPaths":345,"source":320,"sourceLanguage":18,"type":275},"skills/huggingface-paper-publisher","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.","huggingface-paper-publisher",{"claudeCode":342},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[346],{"path":330,"priority":319},{"basePath":348,"description":349,"displayName":350,"installMethods":351,"rationale":352,"selectedPaths":353,"source":320,"sourceLanguage":18,"type":275},"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":350},"inline plugin source from marketplace.json at skills/huggingface-papers",[354],{"path":330,"priority":319},{"basePath":356,"description":357,"displayName":358,"installMethods":359,"rationale":360,"selectedPaths":361,"source":320,"sourceLanguage":18,"type":275},"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":358},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[362],{"path":330,"priority":319},{"basePath":364,"description":365,"displayName":366,"installMethods":367,"rationale":368,"selectedPaths":369,"source":320,"sourceLanguage":18,"type":275},"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":366},"inline plugin source from marketplace.json at skills/huggingface-best",[370],{"path":330,"priority":319},{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":320,"sourceLanguage":18,"type":275},"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":374},"inline plugin source from marketplace.json at skills/hf-cli",[378],{"path":330,"priority":319},{"basePath":260,"description":380,"displayName":262,"installMethods":381,"rationale":382,"selectedPaths":383,"source":320,"sourceLanguage":18,"type":275},"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.",{"claudeCode":262},"inline plugin source from marketplace.json at skills/huggingface-trackio",[384],{"path":330,"priority":319},{"basePath":386,"description":387,"displayName":388,"installMethods":389,"rationale":390,"selectedPaths":391,"source":320,"sourceLanguage":18,"type":275},"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":388},"inline plugin source from marketplace.json at skills/huggingface-datasets",[392],{"path":330,"priority":319},{"basePath":394,"description":395,"displayName":396,"installMethods":397,"rationale":398,"selectedPaths":399,"source":320,"sourceLanguage":18,"type":275},"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":396},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[400],{"path":330,"priority":319},{"basePath":402,"description":403,"displayName":404,"installMethods":405,"rationale":406,"selectedPaths":407,"source":320,"sourceLanguage":18,"type":275},"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":404},"inline plugin source from marketplace.json at skills/huggingface-gradio",[408],{"path":330,"priority":319},{"basePath":410,"description":411,"displayName":412,"installMethods":413,"rationale":414,"selectedPaths":415,"source":320,"sourceLanguage":18,"type":275},"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":412},"inline plugin source from marketplace.json at skills/transformers-js",[416],{"path":330,"priority":319},{"basePath":418,"description":419,"displayName":420,"installMethods":421,"rationale":422,"selectedPaths":423,"source":320,"sourceLanguage":18,"type":275},"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":420},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[424],{"path":330,"priority":319},{"basePath":426,"description":427,"displayName":428,"installMethods":429,"rationale":430,"selectedPaths":431,"source":320,"sourceLanguage":18,"type":275},"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":428},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[432],{"path":330,"priority":319},{"basePath":274,"description":269,"displayName":271,"installMethods":434,"license":254,"rationale":435,"selectedPaths":436,"source":320,"sourceLanguage":18,"type":275},{"claudeCode":271},"plugin manifest at .claude-plugin/plugin.json",[437,439,440,441,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474],{"path":438,"priority":314},".claude-plugin/plugin.json",{"path":316,"priority":314},{"path":318,"priority":319},{"path":442,"priority":443},"skills/hf-cli/SKILL.md","medium",{"path":445,"priority":443},"skills/huggingface-best/SKILL.md",{"path":447,"priority":443},"skills/huggingface-community-evals/SKILL.md",{"path":449,"priority":443},"skills/huggingface-datasets/SKILL.md",{"path":451,"priority":443},"skills/huggingface-gradio/SKILL.md",{"path":453,"priority":443},"skills/huggingface-llm-trainer/SKILL.md",{"path":455,"priority":443},"skills/huggingface-local-models/SKILL.md",{"path":457,"priority":443},"skills/huggingface-paper-publisher/SKILL.md",{"path":459,"priority":443},"skills/huggingface-papers/SKILL.md",{"path":461,"priority":443},"skills/huggingface-tool-builder/SKILL.md",{"path":463,"priority":443},"skills/huggingface-trackio/SKILL.md",{"path":465,"priority":443},"skills/huggingface-vision-trainer/SKILL.md",{"path":467,"priority":443},"skills/train-sentence-transformers/SKILL.md",{"path":469,"priority":443},"skills/transformers-js/SKILL.md",{"path":471,"priority":314},".mcp.json",{"path":473,"priority":319},"agents/AGENTS.md",{"path":475,"priority":319},".cursor-plugin/plugin.json",{"basePath":477,"description":478,"displayName":479,"installMethods":480,"rationale":481,"selectedPaths":482,"source":320,"sourceLanguage":18,"type":263},"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",[483],{"path":330,"priority":314},{"basePath":372,"description":485,"displayName":374,"installMethods":486,"rationale":487,"selectedPaths":488,"source":320,"sourceLanguage":18,"type":263},"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",[489],{"path":330,"priority":314},{"basePath":364,"description":491,"displayName":366,"installMethods":492,"rationale":493,"selectedPaths":494,"source":320,"sourceLanguage":18,"type":263},"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",[495],{"path":330,"priority":314},{"basePath":356,"description":497,"displayName":358,"installMethods":498,"rationale":499,"selectedPaths":500,"source":320,"sourceLanguage":18,"type":263},"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",[501,502,505,507,509,511],{"path":330,"priority":314},{"path":503,"priority":504},"examples/.env.example","low",{"path":506,"priority":504},"examples/USAGE_EXAMPLES.md",{"path":508,"priority":504},"scripts/inspect_eval_uv.py",{"path":510,"priority":504},"scripts/inspect_vllm_uv.py",{"path":512,"priority":504},"scripts/lighteval_vllm_uv.py",{"basePath":386,"description":514,"displayName":388,"installMethods":515,"rationale":516,"selectedPaths":517,"source":320,"sourceLanguage":18,"type":263},"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",[518],{"path":330,"priority":314},{"basePath":402,"description":403,"displayName":404,"installMethods":520,"rationale":521,"selectedPaths":522,"source":320,"sourceLanguage":18,"type":263},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[523,524],{"path":330,"priority":314},{"path":525,"priority":443},"examples.md",{"basePath":323,"description":527,"displayName":325,"installMethods":528,"rationale":529,"selectedPaths":530,"source":320,"sourceLanguage":18,"type":263},"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",[531,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566],{"path":330,"priority":314},{"path":533,"priority":443},"references/gguf_conversion.md",{"path":535,"priority":443},"references/hardware_guide.md",{"path":537,"priority":443},"references/hub_saving.md",{"path":539,"priority":443},"references/local_training_macos.md",{"path":541,"priority":443},"references/reliability_principles.md",{"path":543,"priority":443},"references/trackio_guide.md",{"path":545,"priority":443},"references/training_methods.md",{"path":547,"priority":443},"references/training_patterns.md",{"path":549,"priority":443},"references/troubleshooting.md",{"path":551,"priority":443},"references/unsloth.md",{"path":553,"priority":504},"scripts/convert_to_gguf.py",{"path":555,"priority":504},"scripts/dataset_inspector.py",{"path":557,"priority":504},"scripts/estimate_cost.py",{"path":559,"priority":504},"scripts/hf_benchmarks.py",{"path":561,"priority":504},"scripts/train_dpo_example.py",{"path":563,"priority":504},"scripts/train_grpo_example.py",{"path":565,"priority":504},"scripts/train_sft_example.py",{"path":567,"priority":504},"scripts/unsloth_sft_example.py",{"basePath":332,"description":333,"displayName":334,"installMethods":569,"rationale":570,"selectedPaths":571,"source":320,"sourceLanguage":18,"type":263},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[572,573,575,577],{"path":330,"priority":314},{"path":574,"priority":443},"references/hardware.md",{"path":576,"priority":443},"references/hub-discovery.md",{"path":578,"priority":443},"references/quantization.md",{"basePath":340,"description":341,"displayName":342,"installMethods":580,"rationale":581,"selectedPaths":582,"source":320,"sourceLanguage":18,"type":263},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[583,584,586,588,590,592,594,596],{"path":330,"priority":314},{"path":585,"priority":504},"examples/example_usage.md",{"path":587,"priority":443},"references/quick_reference.md",{"path":589,"priority":504},"scripts/paper_manager.py",{"path":591,"priority":504},"templates/arxiv.md",{"path":593,"priority":504},"templates/ml-report.md",{"path":595,"priority":504},"templates/modern.md",{"path":597,"priority":504},"templates/standard.md",{"basePath":348,"description":599,"displayName":350,"installMethods":600,"rationale":601,"selectedPaths":602,"source":320,"sourceLanguage":18,"type":263},"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",[603],{"path":330,"priority":314},{"basePath":394,"description":605,"displayName":396,"installMethods":606,"rationale":607,"selectedPaths":608,"source":320,"sourceLanguage":18,"type":263},"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",[609,610,612,614,616,618,620,622],{"path":330,"priority":314},{"path":611,"priority":443},"references/baseline_hf_api.py",{"path":613,"priority":443},"references/baseline_hf_api.sh",{"path":615,"priority":443},"references/baseline_hf_api.tsx",{"path":617,"priority":443},"references/find_models_by_paper.sh",{"path":619,"priority":443},"references/hf_enrich_models.sh",{"path":621,"priority":443},"references/hf_model_card_frontmatter.sh",{"path":623,"priority":443},"references/hf_model_papers_auth.sh",{"basePath":260,"description":10,"displayName":262,"installMethods":625,"rationale":626,"selectedPaths":627,"source":320,"sourceLanguage":18,"type":263},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-trackio/SKILL.md",[628,629,631,633],{"path":330,"priority":314},{"path":630,"priority":443},"references/alerts.md",{"path":632,"priority":443},"references/logging_metrics.md",{"path":634,"priority":443},"references/retrieving_metrics.md",{"basePath":418,"description":636,"displayName":420,"installMethods":637,"rationale":638,"selectedPaths":639,"source":320,"sourceLanguage":18,"type":263},"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",[640,641,643,644,646,648,649,651,652,653,655,657],{"path":330,"priority":314},{"path":642,"priority":443},"references/finetune_sam2_trainer.md",{"path":537,"priority":443},{"path":645,"priority":443},"references/image_classification_training_notebook.md",{"path":647,"priority":443},"references/object_detection_training_notebook.md",{"path":541,"priority":443},{"path":650,"priority":443},"references/timm_trainer.md",{"path":555,"priority":504},{"path":557,"priority":504},{"path":654,"priority":504},"scripts/image_classification_training.py",{"path":656,"priority":504},"scripts/object_detection_training.py",{"path":658,"priority":504},"scripts/sam_segmentation_training.py",{"basePath":426,"description":660,"displayName":428,"installMethods":661,"rationale":662,"selectedPaths":663,"source":320,"sourceLanguage":18,"type":263},"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",[664,665,667,669,671,673,675,676,678,680,682,684,686,688,690,691,693,695,697,699,701,703,705,707,709,711,713,715],{"path":330,"priority":314},{"path":666,"priority":443},"references/base_model_selection.md",{"path":668,"priority":443},"references/dataset_formats.md",{"path":670,"priority":443},"references/evaluators_cross_encoder.md",{"path":672,"priority":443},"references/evaluators_sentence_transformer.md",{"path":674,"priority":443},"references/evaluators_sparse_encoder.md",{"path":535,"priority":443},{"path":677,"priority":443},"references/hf_jobs_execution.md",{"path":679,"priority":443},"references/losses_cross_encoder.md",{"path":681,"priority":443},"references/losses_sentence_transformer.md",{"path":683,"priority":443},"references/losses_sparse_encoder.md",{"path":685,"priority":443},"references/model_architectures.md",{"path":687,"priority":443},"references/prompts_and_instructions.md",{"path":689,"priority":443},"references/training_args.md",{"path":549,"priority":443},{"path":692,"priority":504},"scripts/mine_hard_negatives.py",{"path":694,"priority":504},"scripts/train_cross_encoder_distillation_example.py",{"path":696,"priority":504},"scripts/train_cross_encoder_example.py",{"path":698,"priority":504},"scripts/train_cross_encoder_listwise_example.py",{"path":700,"priority":504},"scripts/train_sentence_transformer_distillation_example.py",{"path":702,"priority":504},"scripts/train_sentence_transformer_example.py",{"path":704,"priority":504},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":706,"priority":504},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":708,"priority":504},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":710,"priority":504},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":712,"priority":504},"scripts/train_sentence_transformer_with_lora_example.py",{"path":714,"priority":504},"scripts/train_sparse_encoder_distillation_example.py",{"path":716,"priority":504},"scripts/train_sparse_encoder_example.py",{"basePath":410,"description":718,"displayName":412,"installMethods":719,"rationale":720,"selectedPaths":721,"source":320,"sourceLanguage":18,"type":263},"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",[722,723,725,727,729,731,733,735],{"path":330,"priority":314},{"path":724,"priority":443},"references/CACHE.md",{"path":726,"priority":443},"references/CONFIGURATION.md",{"path":728,"priority":443},"references/EXAMPLES.md",{"path":730,"priority":443},"references/MODEL_ARCHITECTURES.md",{"path":732,"priority":443},"references/MODEL_REGISTRY.md",{"path":734,"priority":443},"references/PIPELINE_OPTIONS.md",{"path":736,"priority":443},"references/TEXT_GENERATION.md",{"sources":738},[739],"manual",{"closedIssues90d":248,"description":741,"forks":249,"homepage":742,"license":254,"openIssues90d":250,"pushedAt":251,"readmeSize":246,"stars":252,"topics":743},"Give your agents the power of the Hugging Face ecosystem","https://huggingface.co",[],{"classifiedAt":745,"discoverAt":746,"extractAt":747,"githubAt":747,"updatedAt":745},1778690772996,1778689536128,1778690770714,[231,229,232,228,230],{"evaluatedAt":258,"extractAt":296,"updatedAt":258},[],[752,782,811,841,869,894],{"_creationTime":753,"_id":754,"community":755,"display":756,"identity":762,"providers":766,"relations":774,"tags":777,"workflow":778},1778696712851.975,"k178yja51cgmcwkj5yctnzbj3186m30w",{"reviewCount":8},{"description":757,"installMethods":758,"name":760,"sourceUrl":761},"Prune bloated session with a prescription. Removes progress ticks, stale reads, duplicate content, and more.",{"claudeCode":759},"Ruya-AI/cozempic","treat","https://github.com/Ruya-AI/cozempic",{"basePath":763,"githubOwner":764,"githubRepo":765,"locale":18,"slug":760,"type":263},"plugin/skills/treat","Ruya-AI","cozempic",{"evaluate":767,"extract":773},{"promptVersionExtension":221,"promptVersionScoring":222,"score":768,"tags":769,"targetMarket":233,"tier":285},100,[770,771,772,230,231],"session-management","context-pruning","llm-optimization",{"commitSha":287},{"parentExtensionId":775,"repoId":776},"k176hd1j2vn0hpak7ds6v3eand86mfqh","kd79d77qmyh3826dwhk7ynx9xd86nmqm",[231,771,772,230,770],{"evaluatedAt":779,"extractAt":780,"updatedAt":781},1778696822903,1778696712852,1778696925366,{"_creationTime":783,"_id":784,"community":785,"display":786,"identity":792,"providers":797,"relations":805,"tags":807,"workflow":808},1778691799740.4775,"k17d0yq6vmmtzk249wz61kpa8n86mqrf",{"reviewCount":8},{"description":787,"installMethods":788,"name":790,"sourceUrl":791},"Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for \"a dataset/model for X\" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say \"Hugging Science\" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.",{"claudeCode":789},"K-Dense-AI/claude-scientific-skills","Hugging Science","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":793,"githubOwner":794,"githubRepo":795,"locale":18,"slug":796,"type":263},"scientific-skills/hugging-science","K-Dense-AI","claude-scientific-skills","hugging-science",{"evaluate":798,"extract":803},{"promptVersionExtension":221,"promptVersionScoring":222,"score":279,"tags":799,"targetMarket":233,"tier":285},[232,800,282,283,801,228,802],"science","research","discovery",{"commitSha":287,"license":804},"MIT",{"repoId":806},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[282,802,232,228,283,801,800],{"evaluatedAt":809,"extractAt":810,"updatedAt":809},1778692838166,1778691799740,{"_creationTime":812,"_id":813,"community":814,"display":815,"identity":821,"providers":826,"relations":834,"tags":837,"workflow":838},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":816,"installMethods":817,"name":819,"sourceUrl":820},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":818},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":822,"githubOwner":823,"githubRepo":824,"locale":18,"slug":825,"type":263},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":827,"extract":833},{"promptVersionExtension":221,"promptVersionScoring":222,"score":768,"tags":828,"targetMarket":233,"tier":285},[829,830,831,281,832,231],"finance","trading","market-analysis","typescript",{"commitSha":287,"license":804},{"parentExtensionId":835,"repoId":836},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[281,231,829,831,830,832],{"evaluatedAt":839,"extractAt":840,"updatedAt":839},1778701108877,1778696691708,{"_creationTime":842,"_id":843,"community":844,"display":845,"identity":851,"providers":855,"relations":862,"tags":865,"workflow":866},1778699234184.6174,"k174zww66m804nhr89ttra7r6d86nwyg",{"reviewCount":8},{"description":846,"installMethods":847,"name":849,"sourceUrl":850},"Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow",{"claudeCode":848},"Yeachan-Heo/oh-my-claudecode","setup","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":852,"githubOwner":853,"githubRepo":854,"locale":18,"slug":849,"type":263},"skills/setup","Yeachan-Heo","oh-my-claudecode",{"evaluate":856,"extract":861},{"promptVersionExtension":221,"promptVersionScoring":222,"score":768,"tags":857,"targetMarket":233,"tier":285},[849,858,859,231,860],"routing","configuration","mcp",{"commitSha":287},{"parentExtensionId":863,"repoId":864},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[231,859,860,858,849],{"evaluatedAt":867,"extractAt":868,"updatedAt":867},1778699724286,1778699234184,{"_creationTime":870,"_id":871,"community":872,"display":873,"identity":877,"providers":880,"relations":890,"tags":891,"workflow":892},1778699234184.6157,"k177tdbfgqmwhtaqv771f2ych586nne9",{"reviewCount":8},{"description":874,"installMethods":875,"name":876,"sourceUrl":850},"Worktree-first dev environment manager for issues, PRs, and features with optional tmux sessions",{"claudeCode":848},"Project Session Manager",{"basePath":878,"githubOwner":853,"githubRepo":854,"locale":18,"slug":879,"type":263},"skills/project-session-manager","project-session-manager",{"evaluate":881,"extract":889},{"promptVersionExtension":221,"promptVersionScoring":222,"score":768,"tags":882,"targetMarket":233,"tier":285},[883,884,885,886,887,231,888],"git","development-environment","workflow","tmux","automation","developer-tool",{"commitSha":287,"license":804},{"parentExtensionId":863,"repoId":864},[887,231,888,884,883,886,885],{"evaluatedAt":893,"extractAt":868,"updatedAt":893},1778699613343,{"_creationTime":895,"_id":896,"community":897,"display":898,"identity":902,"providers":904,"relations":910,"tags":911,"workflow":912},1778699234184.6143,"k17cnx0m6a27fw52yvt4zsbsxh86nd1c",{"reviewCount":8},{"description":899,"installMethods":900,"name":901,"sourceUrl":850},"Configure popular MCP servers for enhanced agent capabilities",{"claudeCode":848},"mcp-setup",{"basePath":903,"githubOwner":853,"githubRepo":854,"locale":18,"slug":901,"type":263},"skills/mcp-setup",{"evaluate":905,"extract":909},{"promptVersionExtension":221,"promptVersionScoring":222,"score":768,"tags":906,"targetMarket":233,"tier":285},[860,859,231,907,908],"agent","tooling",{"commitSha":287},{"parentExtensionId":863,"repoId":864},[907,231,859,860,908],{"evaluatedAt":913,"extractAt":868,"updatedAt":913},1778699492025]