[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-huggingface-datasets-en":3,"guides-for-huggingface-huggingface-datasets":736,"similar-k1718qk3qkn3b221p8505fk9w986nr2t-en":737},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":245,"isFallback":228,"parentExtension":249,"providers":283,"relations":287,"repo":288,"tags":734,"workflow":735},1778690773482.4873,"k1718qk3qkn3b221p8505fk9w986nr2t",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","huggingface-datasets","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":226,"workflow":243},1778691285065.4937,"kn7a9g4njc8caygb2zck9kg20n86n15s","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":202,"promptVersionExtension":207,"promptVersionScoring":208,"purpose":209,"rationale":210,"score":211,"summary":212,"tags":213,"targetMarket":219,"tier":220,"useCases":221},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly identifies a specific user problem: executing workflows related to the Hugging Face Dataset Viewer API for data exploration and extraction.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers specific API calls for dataset manipulation and extraction that go beyond basic Hugging Face Hub interactions, providing specialized value.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill covers the complete lifecycle for viewing and extracting Hugging Face dataset metadata and rows, including discovery, pagination, search, filtering, and download, making it ready for production workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill is focused on the Hugging Face Dataset Viewer API and related data extraction tasks, without straying into unrelated domains like model training or dataset creation.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities for interacting with the Hugging Face Dataset Viewer API, including fetching metadata, paginating, searching, filtering, and downloading data.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes narrowly scoped tools for specific API operations like fetching splits, rows, or statistics, rather than a single generalist execution tool.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md file documents all relevant parameters like dataset, config, split, offset, length, and query, including default values and API endpoint details.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names are descriptive and follow a consistent verb-noun pattern within the dataset domain (e.g., 'validate dataset', 'list subsets and splits').",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool parameters are specific to the requested operation (e.g., dataset, config, split), and responses provide relevant metadata and data without extraneous information.",{"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 and pre-detected license information.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The latest commit was on 2026-05-12, which is within the last 3 months.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","The skill does not appear to use third-party dependencies that require explicit management.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","The skill correctly handles authentication via an optional HF_TOKEN, which is documented to be exported as an environment variable and not echoed in output.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill interacts with the Dataset Viewer API, and all inputs are handled as parameters to API calls, not as executable code.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill relies on the Hugging Face Dataset Viewer REST API and local CLI tools; there are no runtime downloads or executions of external code.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill primarily interacts with a remote API and local CLI tools, making no assumptions or changes to files outside its intended scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were detected in the skill's logic.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill's outbound calls are documented to the Hugging Face API, and it does not exfiltrate confidential data.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content is free of hidden-steering tricks, and descriptions use clean printable ASCII.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The skill's logic is primarily based on API calls and documented CLI usage, with no obfuscated or minified code execution.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The skill makes no assumptions about user project file structure, interacting with remote APIs and documented local CLI commands.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","In the last 90 days, 4 issues were opened and 6 were closed, indicating active maintenance and a healthy closure rate.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The latest commit date indicates active development, and the repository structure implies versioning through commits.",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","The skill's instructions detail parameter usage and API interactions, implying proper validation through API contracts and CLI argument parsing.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The skill is read-only, focusing on data retrieval and exploration, thus posing no risk of destructive operations.",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","The documentation outlines expected API behavior and parameter constraints, implying that errors will be handled by the API or CLI, and the skill's instructions guide the agent on how to interpret them.",{"category":104,"check":114,"severity":63,"summary":115},"Logging","The skill is read-only and does not perform actions that require a local audit log.",{"category":117,"check":118,"severity":63,"summary":119},"Compliance","GDPR","The skill interacts with public Hugging Face dataset APIs and does not process personal data.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The skill interacts with globally accessible Hugging Face APIs and has no regional limitations; targetMarket is 'global'.",{"category":92,"check":124,"severity":24,"summary":125},"Runtime stability","The skill relies on standard Hugging Face APIs and common CLI tools, ensuring broad compatibility across POSIX environments.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README file clearly introduces Hugging Face Skills and provides installation and usage instructions.",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","The skill exposes a focused set of tools (around 8) directly related to dataset viewing and extraction, well within the ideal range.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The tools are distinct and cover specific API functions without significant overlap or near-synonym names.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All features described in the README and SKILL.md have corresponding implemented tools or API interactions.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Installation instructions for various agents (Claude Code, Codex, Gemini CLI, Cursor) are provided, along with clear examples.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","The SKILL.md describes API endpoints and parameters, implying that errors would be reported by the underlying API or CLI, which typically provide descriptive messages and remediation guidance.",{"category":147,"check":148,"severity":63,"summary":149},"Execution","Pinned dependencies","The skill primarily interacts with remote APIs and documented CLI tools, not managing local package dependencies.",{"category":33,"check":151,"severity":24,"summary":152},"Dry-run preview","The skill is read-only and does not perform state-changing operations, making a dry-run feature not applicable.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The skill interacts with APIs that handle their own retries and timeouts; the skill itself does not introduce non-idempotent mutations.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","No telemetry is mentioned or implemented in the skill's code or documentation.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The purpose is precisely defined, naming the artifact (Hugging Face Dataset Viewer API workflows) and the user intent (fetch metadata, paginate, search, filter, download).",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise, self-contained, and clearly states the core capability and its scope.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is concise, under 500 lines, and appropriately delegates detailed information to API endpoint descriptions.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the core workflow and lists API endpoints, providing a level of detail appropriate for progressive disclosure without embedding large external material.",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","The skill is not designed for deep exploration or research; its workflow is short-form and directly maps to API calls.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md includes a clear `curl` example demonstrating pagination, and the API descriptions imply observable outcomes for other endpoints.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The documentation implicitly covers edge cases by detailing API parameters and their expected behavior, including notes on pagination and authentication.",{"category":104,"check":183,"severity":63,"summary":184},"Tool Fallback","The skill does not rely on external tools like an MCP server and only uses standard APIs and documented CLI commands.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The skill operates on immutable API calls and does not modify user state, thus unexpected pre-states are unlikely to affect its operation.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and focuses on the Hugging Face Dataset Viewer API, without implicitly relying on other skills.",1778691284955,"This skill provides programmatic access to the Hugging Face Dataset Viewer API for exploring, querying, and extracting data from Hugging Face datasets. It covers metadata fetching, row pagination, text search, filtering, and downloading parquet URLs without requiring Python dependencies.",[195,196,197,198,199,200,201],"Fetch subset/split metadata","Paginate rows with offset and length","Search text within dataset rows","Apply filters with predicate syntax","Download parquet URLs","Read dataset size and statistics","Validate dataset availability",[203,204,205,206],"Creating or uploading datasets (use hf-cli)","Running ML models","Training or fine-tuning models","Managing Hugging Face Hub resources beyond dataset viewing","3.0.0","4.4.0","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.","The skill is well-documented, secure, and production-ready. It clearly defines its purpose and scope, with a focused set of tools and comprehensive usage examples.",97,"Excellent skill for interacting with Hugging Face Dataset Viewer API.",[214,215,216,217,218],"huggingface","datasets","api","data-exploration","data-extraction","global","verified",[222,223,224,225],"Exploring dataset contents programmatically","Extracting specific subsets of data","Searching for patterns within dataset text","Automating data retrieval for ML tasks",{"codeQuality":227,"collectedAt":229,"documentation":230,"maintenance":233,"security":239,"testCoverage":241},{"hasLockfile":228},false,1778691261707,{"descriptionLength":231,"readmeSize":232},190,9821,{"closedIssues90d":234,"forks":235,"hasChangelog":228,"openIssues90d":236,"pushedAt":237,"stars":238},6,663,4,1778593131000,10482,{"hasNpmPackage":228,"license":240,"smitheryVerified":228},"Apache-2.0",{"hasCi":242,"hasTests":228},true,{"updatedAt":244},1778691285065,{"basePath":246,"githubOwner":214,"githubRepo":247,"locale":18,"slug":13,"type":248},"skills/huggingface-datasets","skills","skill",{"_creationTime":250,"_id":251,"community":252,"display":253,"identity":258,"parentExtension":261,"providers":262,"relations":277,"tags":279,"workflow":280},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":254,"installMethods":255,"name":257,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":256},"huggingface-skills","Hugging Face Skills",{"basePath":259,"githubOwner":214,"githubRepo":247,"locale":18,"slug":247,"type":260},"","plugin",null,{"evaluate":263,"extract":272},{"promptVersionExtension":207,"promptVersionScoring":208,"score":264,"tags":265,"targetMarket":219,"tier":220},98,[214,266,267,215,268,269,270,271],"ai","ml","models","training","cli","python",{"commitSha":273,"license":240,"plugin":274},"HEAD",{"mcpCount":8,"provider":275,"skillCount":276},"classify",14,{"repoId":278},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[266,270,215,214,267,268,271,269],{"evaluatedAt":281,"extractAt":282,"updatedAt":281},1778691185872,1778690773482,{"evaluate":284,"extract":286},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":285,"targetMarket":219,"tier":220},[214,215,216,217,218],{"commitSha":273},{"parentExtensionId":251,"repoId":278},{"_creationTime":289,"_id":278,"identity":290,"providers":291,"workflow":730},1778689536128.5474,{"githubOwner":214,"githubRepo":247,"sourceUrl":14},{"classify":292,"discover":723,"github":726},{"commitSha":273,"extensions":293},[294,308,317,325,333,341,349,357,365,373,379,387,395,403,411,419,462,470,476,482,499,504,511,553,564,583,589,609,621,645,703],{"basePath":259,"description":254,"displayName":256,"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":260},"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":260},"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":326,"description":327,"displayName":328,"installMethods":329,"rationale":330,"selectedPaths":331,"source":306,"sourceLanguage":18,"type":260},"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":328},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[332],{"path":316,"priority":305},{"basePath":334,"description":335,"displayName":336,"installMethods":337,"rationale":338,"selectedPaths":339,"source":306,"sourceLanguage":18,"type":260},"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":336},"inline plugin source from marketplace.json at skills/huggingface-papers",[340],{"path":316,"priority":305},{"basePath":342,"description":343,"displayName":344,"installMethods":345,"rationale":346,"selectedPaths":347,"source":306,"sourceLanguage":18,"type":260},"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":344},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[348],{"path":316,"priority":305},{"basePath":350,"description":351,"displayName":352,"installMethods":353,"rationale":354,"selectedPaths":355,"source":306,"sourceLanguage":18,"type":260},"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":352},"inline plugin source from marketplace.json at skills/huggingface-best",[356],{"path":316,"priority":305},{"basePath":358,"description":359,"displayName":360,"installMethods":361,"rationale":362,"selectedPaths":363,"source":306,"sourceLanguage":18,"type":260},"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":360},"inline plugin source from marketplace.json at skills/hf-cli",[364],{"path":316,"priority":305},{"basePath":366,"description":367,"displayName":368,"installMethods":369,"rationale":370,"selectedPaths":371,"source":306,"sourceLanguage":18,"type":260},"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":368},"inline plugin source from marketplace.json at skills/huggingface-trackio",[372],{"path":316,"priority":305},{"basePath":246,"description":374,"displayName":13,"installMethods":375,"rationale":376,"selectedPaths":377,"source":306,"sourceLanguage":18,"type":260},"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.",{"claudeCode":13},"inline plugin source from marketplace.json at skills/huggingface-datasets",[378],{"path":316,"priority":305},{"basePath":380,"description":381,"displayName":382,"installMethods":383,"rationale":384,"selectedPaths":385,"source":306,"sourceLanguage":18,"type":260},"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":382},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[386],{"path":316,"priority":305},{"basePath":388,"description":389,"displayName":390,"installMethods":391,"rationale":392,"selectedPaths":393,"source":306,"sourceLanguage":18,"type":260},"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":390},"inline plugin source from marketplace.json at skills/huggingface-gradio",[394],{"path":316,"priority":305},{"basePath":396,"description":397,"displayName":398,"installMethods":399,"rationale":400,"selectedPaths":401,"source":306,"sourceLanguage":18,"type":260},"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":398},"inline plugin source from marketplace.json at skills/transformers-js",[402],{"path":316,"priority":305},{"basePath":404,"description":405,"displayName":406,"installMethods":407,"rationale":408,"selectedPaths":409,"source":306,"sourceLanguage":18,"type":260},"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":406},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[410],{"path":316,"priority":305},{"basePath":412,"description":413,"displayName":414,"installMethods":415,"rationale":416,"selectedPaths":417,"source":306,"sourceLanguage":18,"type":260},"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":414},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[418],{"path":316,"priority":305},{"basePath":259,"description":254,"displayName":256,"installMethods":420,"license":240,"rationale":421,"selectedPaths":422,"source":306,"sourceLanguage":18,"type":260},{"claudeCode":256},"plugin manifest at .claude-plugin/plugin.json",[423,425,426,427,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460],{"path":424,"priority":300},".claude-plugin/plugin.json",{"path":302,"priority":300},{"path":304,"priority":305},{"path":428,"priority":429},"skills/hf-cli/SKILL.md","medium",{"path":431,"priority":429},"skills/huggingface-best/SKILL.md",{"path":433,"priority":429},"skills/huggingface-community-evals/SKILL.md",{"path":435,"priority":429},"skills/huggingface-datasets/SKILL.md",{"path":437,"priority":429},"skills/huggingface-gradio/SKILL.md",{"path":439,"priority":429},"skills/huggingface-llm-trainer/SKILL.md",{"path":441,"priority":429},"skills/huggingface-local-models/SKILL.md",{"path":443,"priority":429},"skills/huggingface-paper-publisher/SKILL.md",{"path":445,"priority":429},"skills/huggingface-papers/SKILL.md",{"path":447,"priority":429},"skills/huggingface-tool-builder/SKILL.md",{"path":449,"priority":429},"skills/huggingface-trackio/SKILL.md",{"path":451,"priority":429},"skills/huggingface-vision-trainer/SKILL.md",{"path":453,"priority":429},"skills/train-sentence-transformers/SKILL.md",{"path":455,"priority":429},"skills/transformers-js/SKILL.md",{"path":457,"priority":300},".mcp.json",{"path":459,"priority":305},"agents/AGENTS.md",{"path":461,"priority":305},".cursor-plugin/plugin.json",{"basePath":463,"description":464,"displayName":465,"installMethods":466,"rationale":467,"selectedPaths":468,"source":306,"sourceLanguage":18,"type":248},"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",[469],{"path":316,"priority":300},{"basePath":358,"description":471,"displayName":360,"installMethods":472,"rationale":473,"selectedPaths":474,"source":306,"sourceLanguage":18,"type":248},"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",[475],{"path":316,"priority":300},{"basePath":350,"description":477,"displayName":352,"installMethods":478,"rationale":479,"selectedPaths":480,"source":306,"sourceLanguage":18,"type":248},"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",[481],{"path":316,"priority":300},{"basePath":342,"description":483,"displayName":344,"installMethods":484,"rationale":485,"selectedPaths":486,"source":306,"sourceLanguage":18,"type":248},"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",[487,488,491,493,495,497],{"path":316,"priority":300},{"path":489,"priority":490},"examples/.env.example","low",{"path":492,"priority":490},"examples/USAGE_EXAMPLES.md",{"path":494,"priority":490},"scripts/inspect_eval_uv.py",{"path":496,"priority":490},"scripts/inspect_vllm_uv.py",{"path":498,"priority":490},"scripts/lighteval_vllm_uv.py",{"basePath":246,"description":10,"displayName":13,"installMethods":500,"rationale":501,"selectedPaths":502,"source":306,"sourceLanguage":18,"type":248},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-datasets/SKILL.md",[503],{"path":316,"priority":300},{"basePath":388,"description":389,"displayName":390,"installMethods":505,"rationale":506,"selectedPaths":507,"source":306,"sourceLanguage":18,"type":248},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[508,509],{"path":316,"priority":300},{"path":510,"priority":429},"examples.md",{"basePath":309,"description":512,"displayName":311,"installMethods":513,"rationale":514,"selectedPaths":515,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/gguf_conversion.md",{"path":520,"priority":429},"references/hardware_guide.md",{"path":522,"priority":429},"references/hub_saving.md",{"path":524,"priority":429},"references/local_training_macos.md",{"path":526,"priority":429},"references/reliability_principles.md",{"path":528,"priority":429},"references/trackio_guide.md",{"path":530,"priority":429},"references/training_methods.md",{"path":532,"priority":429},"references/training_patterns.md",{"path":534,"priority":429},"references/troubleshooting.md",{"path":536,"priority":429},"references/unsloth.md",{"path":538,"priority":490},"scripts/convert_to_gguf.py",{"path":540,"priority":490},"scripts/dataset_inspector.py",{"path":542,"priority":490},"scripts/estimate_cost.py",{"path":544,"priority":490},"scripts/hf_benchmarks.py",{"path":546,"priority":490},"scripts/train_dpo_example.py",{"path":548,"priority":490},"scripts/train_grpo_example.py",{"path":550,"priority":490},"scripts/train_sft_example.py",{"path":552,"priority":490},"scripts/unsloth_sft_example.py",{"basePath":318,"description":319,"displayName":320,"installMethods":554,"rationale":555,"selectedPaths":556,"source":306,"sourceLanguage":18,"type":248},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[557,558,560,562],{"path":316,"priority":300},{"path":559,"priority":429},"references/hardware.md",{"path":561,"priority":429},"references/hub-discovery.md",{"path":563,"priority":429},"references/quantization.md",{"basePath":326,"description":327,"displayName":328,"installMethods":565,"rationale":566,"selectedPaths":567,"source":306,"sourceLanguage":18,"type":248},{"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":490},"examples/example_usage.md",{"path":572,"priority":429},"references/quick_reference.md",{"path":574,"priority":490},"scripts/paper_manager.py",{"path":576,"priority":490},"templates/arxiv.md",{"path":578,"priority":490},"templates/ml-report.md",{"path":580,"priority":490},"templates/modern.md",{"path":582,"priority":490},"templates/standard.md",{"basePath":334,"description":584,"displayName":336,"installMethods":585,"rationale":586,"selectedPaths":587,"source":306,"sourceLanguage":18,"type":248},"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":380,"description":590,"displayName":382,"installMethods":591,"rationale":592,"selectedPaths":593,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/baseline_hf_api.py",{"path":598,"priority":429},"references/baseline_hf_api.sh",{"path":600,"priority":429},"references/baseline_hf_api.tsx",{"path":602,"priority":429},"references/find_models_by_paper.sh",{"path":604,"priority":429},"references/hf_enrich_models.sh",{"path":606,"priority":429},"references/hf_model_card_frontmatter.sh",{"path":608,"priority":429},"references/hf_model_papers_auth.sh",{"basePath":366,"description":610,"displayName":368,"installMethods":611,"rationale":612,"selectedPaths":613,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/alerts.md",{"path":618,"priority":429},"references/logging_metrics.md",{"path":620,"priority":429},"references/retrieving_metrics.md",{"basePath":404,"description":622,"displayName":406,"installMethods":623,"rationale":624,"selectedPaths":625,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/finetune_sam2_trainer.md",{"path":522,"priority":429},{"path":631,"priority":429},"references/image_classification_training_notebook.md",{"path":633,"priority":429},"references/object_detection_training_notebook.md",{"path":526,"priority":429},{"path":636,"priority":429},"references/timm_trainer.md",{"path":540,"priority":490},{"path":542,"priority":490},{"path":640,"priority":490},"scripts/image_classification_training.py",{"path":642,"priority":490},"scripts/object_detection_training.py",{"path":644,"priority":490},"scripts/sam_segmentation_training.py",{"basePath":412,"description":646,"displayName":414,"installMethods":647,"rationale":648,"selectedPaths":649,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/base_model_selection.md",{"path":654,"priority":429},"references/dataset_formats.md",{"path":656,"priority":429},"references/evaluators_cross_encoder.md",{"path":658,"priority":429},"references/evaluators_sentence_transformer.md",{"path":660,"priority":429},"references/evaluators_sparse_encoder.md",{"path":520,"priority":429},{"path":663,"priority":429},"references/hf_jobs_execution.md",{"path":665,"priority":429},"references/losses_cross_encoder.md",{"path":667,"priority":429},"references/losses_sentence_transformer.md",{"path":669,"priority":429},"references/losses_sparse_encoder.md",{"path":671,"priority":429},"references/model_architectures.md",{"path":673,"priority":429},"references/prompts_and_instructions.md",{"path":675,"priority":429},"references/training_args.md",{"path":534,"priority":429},{"path":678,"priority":490},"scripts/mine_hard_negatives.py",{"path":680,"priority":490},"scripts/train_cross_encoder_distillation_example.py",{"path":682,"priority":490},"scripts/train_cross_encoder_example.py",{"path":684,"priority":490},"scripts/train_cross_encoder_listwise_example.py",{"path":686,"priority":490},"scripts/train_sentence_transformer_distillation_example.py",{"path":688,"priority":490},"scripts/train_sentence_transformer_example.py",{"path":690,"priority":490},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":692,"priority":490},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":694,"priority":490},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":696,"priority":490},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":698,"priority":490},"scripts/train_sentence_transformer_with_lora_example.py",{"path":700,"priority":490},"scripts/train_sparse_encoder_distillation_example.py",{"path":702,"priority":490},"scripts/train_sparse_encoder_example.py",{"basePath":396,"description":704,"displayName":398,"installMethods":705,"rationale":706,"selectedPaths":707,"source":306,"sourceLanguage":18,"type":248},"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":429},"references/CACHE.md",{"path":712,"priority":429},"references/CONFIGURATION.md",{"path":714,"priority":429},"references/EXAMPLES.md",{"path":716,"priority":429},"references/MODEL_ARCHITECTURES.md",{"path":718,"priority":429},"references/MODEL_REGISTRY.md",{"path":720,"priority":429},"references/PIPELINE_OPTIONS.md",{"path":722,"priority":429},"references/TEXT_GENERATION.md",{"sources":724},[725],"manual",{"closedIssues90d":234,"description":727,"forks":235,"homepage":728,"license":240,"openIssues90d":236,"pushedAt":237,"readmeSize":232,"stars":238,"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,[216,217,218,215,214],{"evaluatedAt":244,"extractAt":282,"updatedAt":244},[],[738,767,786,815,845,871],{"_creationTime":739,"_id":740,"community":741,"display":742,"identity":748,"providers":751,"relations":760,"tags":763,"workflow":764},1778691104676.009,"k178w7wd1nma48cbwy5hbrnq7s86nyvy",{"reviewCount":8},{"description":743,"installMethods":744,"name":746,"sourceUrl":747},"Extract typed JSON from public website pages using a schema.",{"claudeCode":745},"iterationlayer/skills","website-extraction-api","https://github.com/iterationlayer/skills",{"basePath":749,"githubOwner":750,"githubRepo":247,"locale":18,"slug":746,"type":248},"skills/website-extraction-api","iterationlayer",{"evaluate":752,"extract":759},{"promptVersionExtension":207,"promptVersionScoring":208,"score":753,"tags":754,"targetMarket":219,"tier":220},100,[755,218,756,757,216,758],"web-scraping","json","schema","automation",{"commitSha":273},{"parentExtensionId":761,"repoId":762},"k1721s0xmp59902ybtpakrrffn86n10s","kd76p4g2qmtrkgx99cnab3683d86n4g8",[216,758,218,756,757,755],{"evaluatedAt":765,"extractAt":766,"updatedAt":765},1778694012840,1778691104676,{"_creationTime":768,"_id":769,"community":770,"display":771,"identity":775,"providers":777,"relations":782,"tags":783,"workflow":784},1778691104675.9915,"k172qd89p5z3xybe3h8ncdmns586nd5g",{"reviewCount":8},{"description":772,"installMethods":773,"name":774,"sourceUrl":747},"Extract SKUs, product names, unit prices, availability, and minimum order quantities from a supplier catalog page.",{"claudeCode":745},"extract-supplier-catalog-from-website",{"basePath":776,"githubOwner":750,"githubRepo":247,"locale":18,"slug":774,"type":248},"skills/extract-supplier-catalog-from-website",{"evaluate":778,"extract":781},{"promptVersionExtension":207,"promptVersionScoring":208,"score":753,"tags":779,"targetMarket":219,"tier":220},[755,218,780,216,758],"procurement",{"commitSha":273},{"parentExtensionId":761,"repoId":762},[216,758,218,780,755],{"evaluatedAt":785,"extractAt":766,"updatedAt":785},1778692514878,{"_creationTime":787,"_id":788,"community":789,"display":790,"identity":796,"providers":801,"relations":809,"tags":811,"workflow":812},1778691799740.4775,"k17d0yq6vmmtzk249wz61kpa8n86mqrf",{"reviewCount":8},{"description":791,"installMethods":792,"name":794,"sourceUrl":795},"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":793},"K-Dense-AI/claude-scientific-skills","Hugging Science","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":797,"githubOwner":798,"githubRepo":799,"locale":18,"slug":800,"type":248},"scientific-skills/hugging-science","K-Dense-AI","claude-scientific-skills","hugging-science",{"evaluate":802,"extract":807},{"promptVersionExtension":207,"promptVersionScoring":208,"score":264,"tags":803,"targetMarket":219,"tier":220},[214,804,215,268,805,267,806],"science","research","discovery",{"commitSha":273,"license":808},"MIT",{"repoId":810},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[215,806,214,267,268,805,804],{"evaluatedAt":813,"extractAt":814,"updatedAt":813},1778692838166,1778691799740,{"_creationTime":816,"_id":817,"community":818,"display":819,"identity":825,"providers":828,"relations":837,"tags":840,"workflow":841},1778699170774.1592,"k172e8vt4zcz50bb0vfp6ptb1n86mf90",{"reviewCount":8},{"description":820,"installMethods":821,"name":823,"sourceUrl":824},"Use when the user needs X (Twitter) data or confirmation-gated X actions through Xquik: tweet search, user lookup, follower extraction, media download, monitoring, webhooks, MCP, SDKs, posting, likes, DMs, and profile updates. Requires a Xquik API key. Never ask for X login material.",{"claudeCode":822},"Xquik-dev/x-twitter-scraper","x-twitter-scraper","https://github.com/Xquik-dev/x-twitter-scraper",{"basePath":826,"githubOwner":827,"githubRepo":823,"locale":18,"slug":823,"type":248},"skills/x-twitter-scraper","Xquik-dev",{"evaluate":829,"extract":836},{"promptVersionExtension":207,"promptVersionScoring":208,"score":753,"tags":830,"targetMarket":219,"tier":220},[831,832,216,833,758,834,835],"twitter","x","data-retrieval","mcp","sdk",{"commitSha":273},{"parentExtensionId":838,"repoId":839},"k17axvhmvwp90strpqcd5b0h7986m80d","kd783enpnwhry153ka0z65ear186mjbh",[216,758,833,834,835,831,832],{"evaluatedAt":842,"extractAt":843,"updatedAt":844},1778699230863,1778699170774,1778699296021,{"_creationTime":846,"_id":847,"community":848,"display":849,"identity":855,"providers":859,"relations":865,"tags":867,"workflow":868},1778697652123.8982,"k175ckmrqc4x6sjm90k7ejbj3s86ntxs",{"reviewCount":8},{"description":850,"installMethods":851,"name":853,"sourceUrl":854},"Use the Slack tool to react, pin/unpin, send, edit, delete messages, or fetch Slack member info.",{"claudeCode":852},"steipete/clawdis","slack","https://github.com/steipete/clawdis",{"basePath":856,"githubOwner":857,"githubRepo":858,"locale":18,"slug":853,"type":248},"skills/slack","steipete","clawdis",{"evaluate":860,"extract":864},{"promptVersionExtension":207,"promptVersionScoring":208,"score":753,"tags":861,"targetMarket":219,"tier":220},[853,862,863,758,216],"messaging","communication",{"commitSha":273},{"repoId":866},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[216,758,863,862,853],{"evaluatedAt":869,"extractAt":870,"updatedAt":869},1778698950505,1778697652123,{"_creationTime":872,"_id":873,"community":874,"display":875,"identity":879,"providers":881,"relations":886,"tags":887,"workflow":888},1778697652123.8928,"k171pew5empzzrfghyg9nqrk6n86nqa9",{"reviewCount":8},{"description":876,"installMethods":877,"name":878,"sourceUrl":854},"Use gh for GitHub issues, PR status, CI/logs, comments, reviews, releases, and API queries.",{"claudeCode":852},"github",{"basePath":880,"githubOwner":857,"githubRepo":858,"locale":18,"slug":878,"type":248},"skills/github",{"evaluate":882,"extract":885},{"promptVersionExtension":207,"promptVersionScoring":208,"score":753,"tags":883,"targetMarket":219,"tier":220},[878,270,216,884,758],"developer-tools",{"commitSha":273},{"repoId":866},[216,758,270,884,878],{"evaluatedAt":889,"extractAt":870,"updatedAt":889},1778698569289]