[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-huggingface-papers-de":3,"guides-for-huggingface-huggingface-papers":733,"similar-k177asm0v1bhrzc0gq52a936z586memq-de":734},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":241,"isFallback":238,"parentExtension":245,"providers":280,"relations":284,"repo":285,"tags":731,"workflow":732},1778690773482.4885,"k177asm0v1bhrzc0gq52a936z586memq",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","huggingface-papers","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":222,"workflow":239},1778691369571.4326,"kn7cq79j65e4ex738jmyqswy5586nw7n","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":199,"promptVersionExtension":203,"promptVersionScoring":204,"purpose":205,"rationale":206,"score":207,"summary":208,"tags":209,"targetMarket":215,"tier":216,"useCases":217},[21,26,29,32,36,39,43,48,51,54,58,62,65,69,72,75,78,81,84,87,91,95,99,103,107,110,114,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of looking up Hugging Face paper pages and using their API for metadata, and specifies when to use it by providing example URLs and user intents.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill provides a valuable wrapper around Hugging Face's paper pages and APIs, offering structured metadata and markdown content that goes beyond basic web scraping or general LLM knowledge.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill appears production-ready, providing access to paper metadata and markdown content via well-defined API endpoints and clear usage instructions.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses solely on interacting with Hugging Face paper pages and their associated metadata, adhering to a single responsibility.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The description is concise, accurate, and clearly reflects the skill's capabilities and intended use cases.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill utilizes specific API endpoints for fetching paper content and metadata, rather than a general-purpose command.",{"category":44,"check":45,"severity":46,"summary":47},"Documentation","Configuration & parameter reference","not_applicable","The skill does not appear to have configurable parameters or options that require explicit documentation beyond the provided API endpoints.",{"category":33,"check":49,"severity":24,"summary":50},"Tool naming","The skill interacts with Hugging Face APIs, which are implicitly understood as tools, and the description clearly outlines their usage.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The skill's interactions with the Hugging Face API are focused on fetching specific paper data, with clearly defined inputs (paper ID) and outputs (markdown or JSON metadata).",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The extension is licensed under the Apache-2.0 license, which is permissive and usable.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The repository has recent commits, indicating active maintenance.",{"category":59,"check":63,"severity":46,"summary":64},"Dependency Management","The skill does not appear to have external dependencies that require specific management beyond standard API interactions.",{"category":66,"check":67,"severity":46,"summary":68},"Security","Secret Management","The skill does not appear to handle or require secrets for its core functionality of reading public paper data.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill interacts with public APIs and does not appear to load or execute untrusted third-party code or data.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill relies on direct API calls to Hugging Face and does not fetch or execute external code at runtime.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill's operations are limited to making API calls and do not involve file system modifications outside of its designated scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were found in the skill's implementation.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill only interacts with public Hugging Face paper data and does not submit any confidential information.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content and descriptions appear free of hidden-steering tricks.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The skill's implementation does not involve obfuscated code or runtime code fetching.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The skill makes no assumptions about the user's project structure, operating on external URLs and APIs.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","The ratio of closed to open issues is healthy, indicating good engagement from maintainers.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The repository has a recent commit and a clear versioning scheme is implied by the installation instructions referencing specific skills, though no explicit version number is present in the manifest.",{"category":104,"check":105,"severity":24,"summary":106},"Execution","Validation","The skill correctly parses various URL formats and IDs for Hugging Face papers and arXiv, suggesting robust input handling.",{"category":66,"check":108,"severity":46,"summary":109},"Unguarded Destructive Operations","The skill is read-only and does not perform any destructive operations.",{"category":111,"check":112,"severity":24,"summary":113},"Code Execution","Error Handling","The skill includes specific error handling for 404s and paper ID not found scenarios, with suggestions for fallback actions.",{"category":111,"check":115,"severity":46,"summary":116},"Logging","The skill is read-only and does not perform actions that require logging for audit purposes.",{"category":118,"check":119,"severity":46,"summary":120},"Compliance","GDPR","The skill only accesses public paper data and does not handle personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill's functionality is globally applicable and does not have any regional restrictions.",{"category":92,"check":125,"severity":24,"summary":126},"Runtime stability","The skill relies on standard web API interactions and should be stable across different runtime environments.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README file exists and provides a comprehensive overview of the Hugging Face skills repository, including installation and usage instructions.",{"category":33,"check":131,"severity":46,"summary":132},"Tool surface size","This is a single-skill extension, not a collection of tools.",{"category":40,"check":134,"severity":46,"summary":135},"Overlapping near-synonym tools","The skill operates via API calls to Hugging Face, not through a list of distinct tools with potential synonym overlap.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, such as fetching markdown content and structured metadata via API, are implemented and demonstrable.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The README provides clear installation instructions for various agent platforms and includes example invocations.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","Error handling includes specific messages for common issues like 404s and missing paper IDs, with suggested fallback actions.",{"category":104,"check":148,"severity":46,"summary":149},"Pinned dependencies","The skill interacts with external APIs and does not appear to have direct script dependencies that would require pinning.",{"category":33,"check":151,"severity":46,"summary":152},"Dry-run preview","The skill is read-only and does not have state-changing operations that would require a dry-run preview.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The skill relies on standard HTTP requests for API interactions, which inherently support retry mechanisms and timeouts.",{"category":118,"check":158,"severity":46,"summary":159},"Telemetry opt-in","The skill does not appear to emit any telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The description clearly defines the skill's purpose (look up and read Hugging Face paper pages and use the papers API) and its triggers (sharing paper URLs, mentioning arXiv IDs, asking to analyze papers).",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core functionality and usage triggers.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is reasonably concise, detailing API endpoints and error handling without excessive bloat.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the core functionality and API usage, with detailed API endpoint descriptions provided inline, which is appropriate for this skill's scope.",{"category":170,"check":174,"severity":46,"summary":175},"Forked exploration","This skill is not an exploration or audit-style skill; it directly fetches information.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides clear examples of how to fetch paper content as markdown and structured metadata using curl commands.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md documents edge cases like 404 errors and missing paper IDs, with specific fallback recommendations.",{"category":111,"check":183,"severity":46,"summary":184},"Tool Fallback","The skill does not rely on external tools like MCP servers; it directly interacts with Hugging Face APIs.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The skill handles unexpected states such as missing paper IDs or 404 errors by halting the specific action and reporting the issue.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and operates on public Hugging Face APIs, not relying on other specific skills.",1778691369320,"This skill interacts with Hugging Face's paper pages and APIs to retrieve research paper content in markdown format and structured metadata, including authors, linked models, datasets, and GitHub repositories. It can parse various input formats like Hugging Face paper URLs, arXiv URLs, or arXiv IDs.",[195,196,197,198],"Fetch Hugging Face paper pages as markdown","Retrieve structured metadata via Hugging Face Papers API","Parse various paper URL and ID formats (HF, arXiv)","Identify linked models, datasets, and GitHub repositories",[200,201,202],"Analyzing papers not hosted on Hugging Face or arXiv","Providing direct access to the full text of papers not available via the API","Modifying or indexing papers on Hugging Face Hub","3.0.0","4.4.0","To provide users with a reliable way to access and analyze AI research papers hosted on Hugging Face by fetching both formatted content and structured metadata.","Excellent documentation and clear API usage make this a highly reliable skill. No significant issues found.",99,"High-quality skill for accessing and parsing Hugging Face AI research paper information.",[210,211,212,213,214],"huggingface","papers","research","api","arxiv","global","verified",[218,219,220,221],"Summarizing AI research papers shared via URL","Explaining AI research papers from arXiv","Analyzing the metadata of Hugging Face hosted AI papers","Finding linked models and datasets for a given paper",{"codeQuality":223,"collectedAt":225,"documentation":226,"maintenance":229,"security":235,"testCoverage":237},{"hasLockfile":224},false,1778691355299,{"descriptionLength":227,"readmeSize":228},319,9821,{"closedIssues90d":230,"forks":231,"hasChangelog":224,"openIssues90d":232,"pushedAt":233,"stars":234},6,663,4,1778593131000,10482,{"hasNpmPackage":224,"license":236,"smitheryVerified":224},"Apache-2.0",{"hasCi":238,"hasTests":224},true,{"updatedAt":240},1778691369571,{"basePath":242,"githubOwner":210,"githubRepo":243,"locale":18,"slug":13,"type":244},"skills/huggingface-papers","skills","skill",{"_creationTime":246,"_id":247,"community":248,"display":249,"identity":254,"parentExtension":257,"providers":258,"relations":274,"tags":276,"workflow":277},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":250,"installMethods":251,"name":253,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":252},"huggingface-skills","Hugging Face Skills",{"basePath":255,"githubOwner":210,"githubRepo":243,"locale":18,"slug":243,"type":256},"","plugin",null,{"evaluate":259,"extract":269},{"promptVersionExtension":203,"promptVersionScoring":204,"score":260,"tags":261,"targetMarket":215,"tier":216},98,[210,262,263,264,265,266,267,268],"ai","ml","datasets","models","training","cli","python",{"commitSha":270,"license":236,"plugin":271},"HEAD",{"mcpCount":8,"provider":272,"skillCount":273},"classify",14,{"repoId":275},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[262,267,264,210,263,265,268,266],{"evaluatedAt":278,"extractAt":279,"updatedAt":278},1778691185872,1778690773482,{"evaluate":281,"extract":283},{"promptVersionExtension":203,"promptVersionScoring":204,"score":207,"tags":282,"targetMarket":215,"tier":216},[210,211,212,213,214],{"commitSha":270},{"parentExtensionId":247,"repoId":275},{"_creationTime":286,"_id":275,"identity":287,"providers":288,"workflow":727},1778689536128.5474,{"githubOwner":210,"githubRepo":243,"sourceUrl":14},{"classify":289,"discover":720,"github":723},{"commitSha":270,"extensions":290},[291,305,314,322,330,336,344,352,360,368,376,384,392,400,408,416,459,467,473,479,496,502,509,551,562,581,586,606,618,642,700],{"basePath":255,"description":250,"displayName":252,"installMethods":292,"rationale":293,"selectedPaths":294,"source":303,"sourceLanguage":18,"type":304},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[295,298,300],{"path":296,"priority":297},".claude-plugin/marketplace.json","mandatory",{"path":299,"priority":297},"README.md",{"path":301,"priority":302},"LICENSE","high","rule","marketplace",{"basePath":306,"description":307,"displayName":308,"installMethods":309,"rationale":310,"selectedPaths":311,"source":303,"sourceLanguage":18,"type":256},"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":308},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[312],{"path":313,"priority":302},"SKILL.md",{"basePath":315,"description":316,"displayName":317,"installMethods":318,"rationale":319,"selectedPaths":320,"source":303,"sourceLanguage":18,"type":256},"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":317},"inline plugin source from marketplace.json at skills/huggingface-local-models",[321],{"path":313,"priority":302},{"basePath":323,"description":324,"displayName":325,"installMethods":326,"rationale":327,"selectedPaths":328,"source":303,"sourceLanguage":18,"type":256},"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":325},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[329],{"path":313,"priority":302},{"basePath":242,"description":331,"displayName":13,"installMethods":332,"rationale":333,"selectedPaths":334,"source":303,"sourceLanguage":18,"type":256},"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.",{"claudeCode":13},"inline plugin source from marketplace.json at skills/huggingface-papers",[335],{"path":313,"priority":302},{"basePath":337,"description":338,"displayName":339,"installMethods":340,"rationale":341,"selectedPaths":342,"source":303,"sourceLanguage":18,"type":256},"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":339},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[343],{"path":313,"priority":302},{"basePath":345,"description":346,"displayName":347,"installMethods":348,"rationale":349,"selectedPaths":350,"source":303,"sourceLanguage":18,"type":256},"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":347},"inline plugin source from marketplace.json at skills/huggingface-best",[351],{"path":313,"priority":302},{"basePath":353,"description":354,"displayName":355,"installMethods":356,"rationale":357,"selectedPaths":358,"source":303,"sourceLanguage":18,"type":256},"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":355},"inline plugin source from marketplace.json at skills/hf-cli",[359],{"path":313,"priority":302},{"basePath":361,"description":362,"displayName":363,"installMethods":364,"rationale":365,"selectedPaths":366,"source":303,"sourceLanguage":18,"type":256},"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":363},"inline plugin source from marketplace.json at skills/huggingface-trackio",[367],{"path":313,"priority":302},{"basePath":369,"description":370,"displayName":371,"installMethods":372,"rationale":373,"selectedPaths":374,"source":303,"sourceLanguage":18,"type":256},"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":371},"inline plugin source from marketplace.json at skills/huggingface-datasets",[375],{"path":313,"priority":302},{"basePath":377,"description":378,"displayName":379,"installMethods":380,"rationale":381,"selectedPaths":382,"source":303,"sourceLanguage":18,"type":256},"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":379},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[383],{"path":313,"priority":302},{"basePath":385,"description":386,"displayName":387,"installMethods":388,"rationale":389,"selectedPaths":390,"source":303,"sourceLanguage":18,"type":256},"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":387},"inline plugin source from marketplace.json at skills/huggingface-gradio",[391],{"path":313,"priority":302},{"basePath":393,"description":394,"displayName":395,"installMethods":396,"rationale":397,"selectedPaths":398,"source":303,"sourceLanguage":18,"type":256},"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":395},"inline plugin source from marketplace.json at skills/transformers-js",[399],{"path":313,"priority":302},{"basePath":401,"description":402,"displayName":403,"installMethods":404,"rationale":405,"selectedPaths":406,"source":303,"sourceLanguage":18,"type":256},"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":403},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[407],{"path":313,"priority":302},{"basePath":409,"description":410,"displayName":411,"installMethods":412,"rationale":413,"selectedPaths":414,"source":303,"sourceLanguage":18,"type":256},"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":411},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[415],{"path":313,"priority":302},{"basePath":255,"description":250,"displayName":252,"installMethods":417,"license":236,"rationale":418,"selectedPaths":419,"source":303,"sourceLanguage":18,"type":256},{"claudeCode":252},"plugin manifest at .claude-plugin/plugin.json",[420,422,423,424,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457],{"path":421,"priority":297},".claude-plugin/plugin.json",{"path":299,"priority":297},{"path":301,"priority":302},{"path":425,"priority":426},"skills/hf-cli/SKILL.md","medium",{"path":428,"priority":426},"skills/huggingface-best/SKILL.md",{"path":430,"priority":426},"skills/huggingface-community-evals/SKILL.md",{"path":432,"priority":426},"skills/huggingface-datasets/SKILL.md",{"path":434,"priority":426},"skills/huggingface-gradio/SKILL.md",{"path":436,"priority":426},"skills/huggingface-llm-trainer/SKILL.md",{"path":438,"priority":426},"skills/huggingface-local-models/SKILL.md",{"path":440,"priority":426},"skills/huggingface-paper-publisher/SKILL.md",{"path":442,"priority":426},"skills/huggingface-papers/SKILL.md",{"path":444,"priority":426},"skills/huggingface-tool-builder/SKILL.md",{"path":446,"priority":426},"skills/huggingface-trackio/SKILL.md",{"path":448,"priority":426},"skills/huggingface-vision-trainer/SKILL.md",{"path":450,"priority":426},"skills/train-sentence-transformers/SKILL.md",{"path":452,"priority":426},"skills/transformers-js/SKILL.md",{"path":454,"priority":297},".mcp.json",{"path":456,"priority":302},"agents/AGENTS.md",{"path":458,"priority":302},".cursor-plugin/plugin.json",{"basePath":460,"description":461,"displayName":462,"installMethods":463,"rationale":464,"selectedPaths":465,"source":303,"sourceLanguage":18,"type":244},"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",[466],{"path":313,"priority":297},{"basePath":353,"description":468,"displayName":355,"installMethods":469,"rationale":470,"selectedPaths":471,"source":303,"sourceLanguage":18,"type":244},"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",[472],{"path":313,"priority":297},{"basePath":345,"description":474,"displayName":347,"installMethods":475,"rationale":476,"selectedPaths":477,"source":303,"sourceLanguage":18,"type":244},"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",[478],{"path":313,"priority":297},{"basePath":337,"description":480,"displayName":339,"installMethods":481,"rationale":482,"selectedPaths":483,"source":303,"sourceLanguage":18,"type":244},"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",[484,485,488,490,492,494],{"path":313,"priority":297},{"path":486,"priority":487},"examples/.env.example","low",{"path":489,"priority":487},"examples/USAGE_EXAMPLES.md",{"path":491,"priority":487},"scripts/inspect_eval_uv.py",{"path":493,"priority":487},"scripts/inspect_vllm_uv.py",{"path":495,"priority":487},"scripts/lighteval_vllm_uv.py",{"basePath":369,"description":497,"displayName":371,"installMethods":498,"rationale":499,"selectedPaths":500,"source":303,"sourceLanguage":18,"type":244},"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",[501],{"path":313,"priority":297},{"basePath":385,"description":386,"displayName":387,"installMethods":503,"rationale":504,"selectedPaths":505,"source":303,"sourceLanguage":18,"type":244},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[506,507],{"path":313,"priority":297},{"path":508,"priority":426},"examples.md",{"basePath":306,"description":510,"displayName":308,"installMethods":511,"rationale":512,"selectedPaths":513,"source":303,"sourceLanguage":18,"type":244},"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",[514,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549],{"path":313,"priority":297},{"path":516,"priority":426},"references/gguf_conversion.md",{"path":518,"priority":426},"references/hardware_guide.md",{"path":520,"priority":426},"references/hub_saving.md",{"path":522,"priority":426},"references/local_training_macos.md",{"path":524,"priority":426},"references/reliability_principles.md",{"path":526,"priority":426},"references/trackio_guide.md",{"path":528,"priority":426},"references/training_methods.md",{"path":530,"priority":426},"references/training_patterns.md",{"path":532,"priority":426},"references/troubleshooting.md",{"path":534,"priority":426},"references/unsloth.md",{"path":536,"priority":487},"scripts/convert_to_gguf.py",{"path":538,"priority":487},"scripts/dataset_inspector.py",{"path":540,"priority":487},"scripts/estimate_cost.py",{"path":542,"priority":487},"scripts/hf_benchmarks.py",{"path":544,"priority":487},"scripts/train_dpo_example.py",{"path":546,"priority":487},"scripts/train_grpo_example.py",{"path":548,"priority":487},"scripts/train_sft_example.py",{"path":550,"priority":487},"scripts/unsloth_sft_example.py",{"basePath":315,"description":316,"displayName":317,"installMethods":552,"rationale":553,"selectedPaths":554,"source":303,"sourceLanguage":18,"type":244},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[555,556,558,560],{"path":313,"priority":297},{"path":557,"priority":426},"references/hardware.md",{"path":559,"priority":426},"references/hub-discovery.md",{"path":561,"priority":426},"references/quantization.md",{"basePath":323,"description":324,"displayName":325,"installMethods":563,"rationale":564,"selectedPaths":565,"source":303,"sourceLanguage":18,"type":244},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[566,567,569,571,573,575,577,579],{"path":313,"priority":297},{"path":568,"priority":487},"examples/example_usage.md",{"path":570,"priority":426},"references/quick_reference.md",{"path":572,"priority":487},"scripts/paper_manager.py",{"path":574,"priority":487},"templates/arxiv.md",{"path":576,"priority":487},"templates/ml-report.md",{"path":578,"priority":487},"templates/modern.md",{"path":580,"priority":487},"templates/standard.md",{"basePath":242,"description":10,"displayName":13,"installMethods":582,"rationale":583,"selectedPaths":584,"source":303,"sourceLanguage":18,"type":244},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-papers/SKILL.md",[585],{"path":313,"priority":297},{"basePath":377,"description":587,"displayName":379,"installMethods":588,"rationale":589,"selectedPaths":590,"source":303,"sourceLanguage":18,"type":244},"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",[591,592,594,596,598,600,602,604],{"path":313,"priority":297},{"path":593,"priority":426},"references/baseline_hf_api.py",{"path":595,"priority":426},"references/baseline_hf_api.sh",{"path":597,"priority":426},"references/baseline_hf_api.tsx",{"path":599,"priority":426},"references/find_models_by_paper.sh",{"path":601,"priority":426},"references/hf_enrich_models.sh",{"path":603,"priority":426},"references/hf_model_card_frontmatter.sh",{"path":605,"priority":426},"references/hf_model_papers_auth.sh",{"basePath":361,"description":607,"displayName":363,"installMethods":608,"rationale":609,"selectedPaths":610,"source":303,"sourceLanguage":18,"type":244},"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",[611,612,614,616],{"path":313,"priority":297},{"path":613,"priority":426},"references/alerts.md",{"path":615,"priority":426},"references/logging_metrics.md",{"path":617,"priority":426},"references/retrieving_metrics.md",{"basePath":401,"description":619,"displayName":403,"installMethods":620,"rationale":621,"selectedPaths":622,"source":303,"sourceLanguage":18,"type":244},"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",[623,624,626,627,629,631,632,634,635,636,638,640],{"path":313,"priority":297},{"path":625,"priority":426},"references/finetune_sam2_trainer.md",{"path":520,"priority":426},{"path":628,"priority":426},"references/image_classification_training_notebook.md",{"path":630,"priority":426},"references/object_detection_training_notebook.md",{"path":524,"priority":426},{"path":633,"priority":426},"references/timm_trainer.md",{"path":538,"priority":487},{"path":540,"priority":487},{"path":637,"priority":487},"scripts/image_classification_training.py",{"path":639,"priority":487},"scripts/object_detection_training.py",{"path":641,"priority":487},"scripts/sam_segmentation_training.py",{"basePath":409,"description":643,"displayName":411,"installMethods":644,"rationale":645,"selectedPaths":646,"source":303,"sourceLanguage":18,"type":244},"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",[647,648,650,652,654,656,658,659,661,663,665,667,669,671,673,674,676,678,680,682,684,686,688,690,692,694,696,698],{"path":313,"priority":297},{"path":649,"priority":426},"references/base_model_selection.md",{"path":651,"priority":426},"references/dataset_formats.md",{"path":653,"priority":426},"references/evaluators_cross_encoder.md",{"path":655,"priority":426},"references/evaluators_sentence_transformer.md",{"path":657,"priority":426},"references/evaluators_sparse_encoder.md",{"path":518,"priority":426},{"path":660,"priority":426},"references/hf_jobs_execution.md",{"path":662,"priority":426},"references/losses_cross_encoder.md",{"path":664,"priority":426},"references/losses_sentence_transformer.md",{"path":666,"priority":426},"references/losses_sparse_encoder.md",{"path":668,"priority":426},"references/model_architectures.md",{"path":670,"priority":426},"references/prompts_and_instructions.md",{"path":672,"priority":426},"references/training_args.md",{"path":532,"priority":426},{"path":675,"priority":487},"scripts/mine_hard_negatives.py",{"path":677,"priority":487},"scripts/train_cross_encoder_distillation_example.py",{"path":679,"priority":487},"scripts/train_cross_encoder_example.py",{"path":681,"priority":487},"scripts/train_cross_encoder_listwise_example.py",{"path":683,"priority":487},"scripts/train_sentence_transformer_distillation_example.py",{"path":685,"priority":487},"scripts/train_sentence_transformer_example.py",{"path":687,"priority":487},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":689,"priority":487},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":691,"priority":487},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":693,"priority":487},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":695,"priority":487},"scripts/train_sentence_transformer_with_lora_example.py",{"path":697,"priority":487},"scripts/train_sparse_encoder_distillation_example.py",{"path":699,"priority":487},"scripts/train_sparse_encoder_example.py",{"basePath":393,"description":701,"displayName":395,"installMethods":702,"rationale":703,"selectedPaths":704,"source":303,"sourceLanguage":18,"type":244},"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",[705,706,708,710,712,714,716,718],{"path":313,"priority":297},{"path":707,"priority":426},"references/CACHE.md",{"path":709,"priority":426},"references/CONFIGURATION.md",{"path":711,"priority":426},"references/EXAMPLES.md",{"path":713,"priority":426},"references/MODEL_ARCHITECTURES.md",{"path":715,"priority":426},"references/MODEL_REGISTRY.md",{"path":717,"priority":426},"references/PIPELINE_OPTIONS.md",{"path":719,"priority":426},"references/TEXT_GENERATION.md",{"sources":721},[722],"manual",{"closedIssues90d":230,"description":724,"forks":231,"homepage":725,"license":236,"openIssues90d":232,"pushedAt":233,"readmeSize":228,"stars":234,"topics":726},"Give your agents the power of the Hugging Face ecosystem","https://huggingface.co",[],{"classifiedAt":728,"discoverAt":729,"extractAt":730,"githubAt":730,"updatedAt":728},1778690772996,1778689536128,1778690770714,[213,214,210,211,212],{"evaluatedAt":240,"extractAt":279,"updatedAt":240},[],[735,769,795,814,842,868],{"_creationTime":736,"_id":737,"community":738,"display":739,"identity":745,"providers":749,"relations":760,"tags":764,"workflow":765},1778699276507.4565,"k17f878eas78tqr9j5r4r9vrsn86m495",{"reviewCount":8},{"description":740,"installMethods":741,"name":743,"sourceUrl":744},"Verwenden Sie dies, wenn der Benutzer X (Twitter)-Daten oder durch Bestätigung gesicherte X-Aktionen über Xquik benötigt: Tweet-Suche, Benutzer-Lookup, Follower-Extraktion, Mediendownload, Überwachung, Webhooks, MCP, SDKs, Posting, Likes, DMs und Profilaktualisierungen. Erfordert einen Xquik API-Schlüssel. Fordern Sie niemals X-Login-Materialien an.",{"claudeCode":742},"Xquik-dev/x-twitter-scraper","x-twitter-scraper","https://github.com/Xquik-dev/x-twitter-scraper",{"basePath":746,"githubOwner":747,"githubRepo":743,"locale":748,"slug":743,"type":244},"skills/x-twitter-scraper","Xquik-dev","de",{"evaluate":750,"extract":759},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":752,"targetMarket":215,"tier":216},100,[753,754,213,755,756,757,758],"twitter","x","data-retrieval","automation","mcp","sdk",{"commitSha":270},{"parentExtensionId":761,"repoId":762,"translatedFrom":763},"k17axvhmvwp90strpqcd5b0h7986m80d","kd783enpnwhry153ka0z65ear186mjbh","k172e8vt4zcz50bb0vfp6ptb1n86mf90",[213,756,755,757,758,753,754],{"evaluatedAt":766,"extractAt":767,"updatedAt":768},1778699230863,1778699170774,1778699276507,{"_creationTime":770,"_id":771,"community":772,"display":773,"identity":779,"providers":783,"relations":789,"tags":791,"workflow":792},1778697652123.8982,"k175ckmrqc4x6sjm90k7ejbj3s86ntxs",{"reviewCount":8},{"description":774,"installMethods":775,"name":777,"sourceUrl":778},"Use the Slack tool to react, pin/unpin, send, edit, delete messages, or fetch Slack member info.",{"claudeCode":776},"steipete/clawdis","slack","https://github.com/steipete/clawdis",{"basePath":780,"githubOwner":781,"githubRepo":782,"locale":18,"slug":777,"type":244},"skills/slack","steipete","clawdis",{"evaluate":784,"extract":788},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":785,"targetMarket":215,"tier":216},[777,786,787,756,213],"messaging","communication",{"commitSha":270},{"repoId":790},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[213,756,787,786,777],{"evaluatedAt":793,"extractAt":794,"updatedAt":793},1778698950505,1778697652123,{"_creationTime":796,"_id":797,"community":798,"display":799,"identity":803,"providers":805,"relations":810,"tags":811,"workflow":812},1778697652123.8928,"k171pew5empzzrfghyg9nqrk6n86nqa9",{"reviewCount":8},{"description":800,"installMethods":801,"name":802,"sourceUrl":778},"Use gh for GitHub issues, PR status, CI/logs, comments, reviews, releases, and API queries.",{"claudeCode":776},"github",{"basePath":804,"githubOwner":781,"githubRepo":782,"locale":18,"slug":802,"type":244},"skills/github",{"evaluate":806,"extract":809},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":807,"targetMarket":215,"tier":216},[802,267,213,808,756],"developer-tools",{"commitSha":270},{"repoId":790},[213,756,267,808,802],{"evaluatedAt":813,"extractAt":794,"updatedAt":813},1778698569289,{"_creationTime":815,"_id":816,"community":817,"display":818,"identity":824,"providers":828,"relations":836,"tags":838,"workflow":839},1778696993586.708,"k17fsfrfvbnsvwkcqp8y85wdad86mmwq",{"reviewCount":8},{"description":819,"installMethods":820,"name":822,"sourceUrl":823},"Stop and consult this skill whenever your response would include specific facts about Anthropic's products. 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Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.",{"claudeCode":821},"SeifBenayed/claude-code-sdk","product-self-knowledge","https://github.com/SeifBenayed/claude-code-sdk",{"basePath":825,"githubOwner":826,"githubRepo":827,"locale":18,"slug":822,"type":244},".claude/skills/product-self-knowledge","SeifBenayed","claude-code-sdk",{"evaluate":829,"extract":835},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":830,"targetMarket":215,"tier":216},[831,832,833,213,758,834],"anthropic","documentation","claude","knowledge-base",{"commitSha":270},{"repoId":837},"kd78s53c1852h5p7c3qem663xs86njab",[831,213,833,832,834,758],{"evaluatedAt":840,"extractAt":841,"updatedAt":840},1778697182451,1778696993586,{"_creationTime":843,"_id":844,"community":845,"display":846,"identity":852,"providers":856,"relations":861,"tags":864,"workflow":865},1778696833339.6226,"k17ckxne6mhyf23n1jfyqktpqd86nfz4",{"reviewCount":8},{"description":847,"installMethods":848,"name":850,"sourceUrl":851},"Interact with Google Docs - create documents, search by title, read content, and edit text.\nUse when user asks to: create a Google Doc, find a document, read doc content, add text to a doc,\nor replace text in a document. Lightweight alternative to full Google Workspace MCP server with\nstandalone OAuth authentication.\n",{"claudeCode":849},"sanjay3290/ai-skills","google-docs","https://github.com/sanjay3290/ai-skills",{"basePath":853,"githubOwner":854,"githubRepo":855,"locale":18,"slug":850,"type":244},"skills/google-docs","sanjay3290","ai-skills",{"evaluate":857,"extract":860},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":858,"targetMarket":215,"tier":216},[850,213,832,859,268],"oauth",{"commitSha":270},{"parentExtensionId":862,"repoId":863},"k17es37z10n1sw6t2m3f0vsydx86mnje","kd71np0fyqg23qg8w2hcfw0h0h86nkn0",[213,832,850,859,268],{"evaluatedAt":866,"extractAt":867,"updatedAt":866},1778696994497,1778696833339,{"_creationTime":869,"_id":870,"community":871,"display":872,"identity":878,"providers":884,"relations":890,"tags":893,"workflow":894},1778696505500.0078,"k174n9sd7wv9knh3b8rv7vv2wh86me74",{"reviewCount":8},{"description":873,"installMethods":874,"name":876,"sourceUrl":877},"Search and retrieve content from Reddit. Get posts, comments, subreddit info, and user profiles via the public JSON API. Use when user mentions Reddit, a subreddit, or r/ links.",{"claudeCode":875},"ReScienceLab/opc-skills","Reddit","https://github.com/ReScienceLab/opc-skills",{"basePath":879,"githubOwner":880,"githubRepo":881,"locale":882,"slug":883,"type":244},"skills/reddit","ReScienceLab","opc-skills","fr","reddit",{"evaluate":885,"extract":889},{"promptVersionExtension":203,"promptVersionScoring":204,"score":751,"tags":886,"targetMarket":215,"tier":216},[883,213,755,887,888],"social-media","information-gathering",{"commitSha":270,"license":236},{"parentExtensionId":891,"repoId":892},"k17b55rp7ccqw91566yq0ax2as86n6rk","kd7fj56h5kejcgm6hcjmzn79xd86m7wa",[213,755,888,883,887],{"evaluatedAt":895,"extractAt":896,"updatedAt":895},1778696852717,1778696505500]