[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-plugin-huggingface-huggingface-best-zh-CN":3,"guides-for-huggingface-huggingface-best":746,"similar-k175xxwhkryry1vj3f64maceh586npj6-zh-CN":747},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":14,"identity":254,"isFallback":251,"parentExtension":258,"providers":293,"relations":297,"repo":298,"tags":744,"workflow":745},1778690773482.484,"k175xxwhkryry1vj3f64maceh586npj6",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":12,"sourceUrl":13},"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.",{"claudeCode":12},"huggingface-best","https://github.com/huggingface/skills",{"_creationTime":15,"_id":16,"extensionId":5,"locale":17,"result":18,"trustSignals":235,"workflow":252},1778690943353.1016,"kn7dne2tf72qmzr8ay5mx57k3s86n9ep","en",{"checks":19,"evaluatedAt":202,"extensionSummary":203,"features":204,"nonGoals":210,"promptVersionExtension":215,"promptVersionScoring":216,"purpose":217,"rationale":218,"score":219,"summary":220,"tags":221,"targetMarket":228,"tier":229,"useCases":230},[20,25,28,31,35,38,43,47,50,53,57,61,64,69,72,75,78,81,85,88,92,96,100,104,108,111,114,117,121,124,127,130,133,136,139,143,147,151,154,158,161,164,167,170,173,176,179,182,185,188,192,195,198],{"category":21,"check":22,"severity":23,"summary":24},"Practical Utility","Problem relevance","pass","The description clearly articulates the user problem of finding the best AI model for a task, considering various constraints.",{"category":21,"check":26,"severity":23,"summary":27},"Unique selling proposition","The skill goes beyond a simple API wrapper by intelligently querying leaderboards, filtering by device constraints, and providing a comparative analysis.",{"category":21,"check":29,"severity":23,"summary":30},"Production readiness","The skill provides a complete workflow for model selection, including parsing requests, fetching data, enriching metadata, filtering, and outputting a comparison table, making it ready for use.",{"category":32,"check":33,"severity":23,"summary":34},"Scope","Single responsibility principle","The plugin focuses on recommending AI models based on Hugging Face benchmarks and user constraints, adhering to a single responsibility.",{"category":32,"check":36,"severity":23,"summary":37},"Description quality","The displayed description accurately reflects the skill's functionality of recommending AI models based on benchmarks and user constraints.",{"category":39,"check":40,"severity":41,"summary":42},"Invocation","Scoped tools","not_applicable","This is a plugin, not a collection of individual tools. Evaluating individual tool scoping is not applicable here.",{"category":44,"check":45,"severity":41,"summary":46},"Documentation","Configuration & parameter reference","The skill itself does not appear to have configurable parameters or explicit configuration files beyond what might be handled by the underlying agent framework.",{"category":32,"check":48,"severity":41,"summary":49},"Tool naming","This is a plugin evaluated for its overall functionality, not individual tools.",{"category":32,"check":51,"severity":23,"summary":52},"Minimal I/O surface","The skill's inputs (task, device constraints) and outputs (comparison table, follow-up questions) are well-defined and directly relevant to its purpose.",{"category":54,"check":55,"severity":23,"summary":56},"License","License usability","The repository provides an Apache-2.0 license file, which is a permissive open-source license.",{"category":58,"check":59,"severity":23,"summary":60},"Maintenance","Commit recency","The last commit was on May 12, 2026, which is within the last 90 days.",{"category":58,"check":62,"severity":41,"summary":63},"Dependency Management","The skill relies on external tools like `curl` and `jq` and Hugging Face APIs, rather than bundled third-party dependencies that require explicit management.",{"category":65,"check":66,"severity":67,"summary":68},"Security","Secret Management","warning","The skill script references `~/.cache/huggingface/token` and `$(cat ~/.cache/huggingface/token)` for API access, suggesting it might expose or handle user tokens without clear indication of secure handling.",{"category":65,"check":70,"severity":23,"summary":71},"Injection","The skill processes user input to extract task and device information, but it does not appear to execute arbitrary code or load external, untrusted instructions.",{"category":65,"check":73,"severity":23,"summary":74},"Transitive Supply-Chain Grenades","The skill uses `curl` to fetch data from Hugging Face APIs and standard CLI tools, which are assumed to be secure and part of the runtime environment, rather than fetching and executing arbitrary remote code.",{"category":65,"check":76,"severity":23,"summary":77},"Sandbox Isolation","The skill primarily interacts with external APIs and local files for configuration, without evidence of attempting to modify files outside its intended scope.",{"category":65,"check":79,"severity":23,"summary":80},"Sandbox escape primitives","The skill's script does not appear to contain any detached-process spawns or retry loops around denied tool calls.",{"category":65,"check":82,"severity":83,"summary":84},"Data Exfiltration","info","The skill reads a Hugging Face token from a local file for API authentication, which is a common practice but should ideally be handled more securely.",{"category":65,"check":86,"severity":23,"summary":87},"Hidden Text Tricks","The bundled content and scripts do not appear to contain any hidden steering tricks, invisible Unicode characters, or other obfuscation methods.",{"category":89,"check":90,"severity":23,"summary":91},"Hooks","Opaque code execution","The skill script is a readable bash script and does not involve obfuscation techniques like base64 decoding or runtime script fetching.",{"category":93,"check":94,"severity":23,"summary":95},"Portability","Structural Assumption","The skill makes reasonable assumptions about the presence of standard tools like `curl` and `jq` and the existence of Hugging Face API tokens, which are documented or expected in the agent environment.",{"category":97,"check":98,"severity":23,"summary":99},"Trust","Issues Attention","In the last 90 days, 4 issues were opened and 6 were closed, indicating active maintenance and a reasonable closure rate.",{"category":101,"check":102,"severity":23,"summary":103},"Versioning","Release Management","The repository has recent commits and a clear `Apache-2.0` license, suggesting active management, though a formal versioning scheme is not explicitly stated in the README.",{"category":105,"check":106,"severity":67,"summary":107},"Code Execution","Validation","The skill parses user input for task and device, but it does not appear to use a schema validation library for input sanitization or parameter validation.",{"category":65,"check":109,"severity":23,"summary":110},"Unguarded Destructive Operations","The skill is read-only, focusing on querying information and making recommendations, thus it does not perform any destructive operations.",{"category":105,"check":112,"severity":67,"summary":113},"Error Handling","The skill includes basic error handling for API calls (e.g., 'leaderboard not found'), but it lacks structured error reporting with retryable flags or hints for the agent.",{"category":105,"check":115,"severity":41,"summary":116},"Logging","The skill performs read-only operations and does not seem to require local audit logging for user review.",{"category":118,"check":119,"severity":41,"summary":120},"Compliance","GDPR","The skill does not operate on personal data; it queries public Hugging Face benchmarks and model information.",{"category":118,"check":122,"severity":23,"summary":123},"Target market","The skill's functionality is global and not tied to any specific geographic or legal jurisdiction.",{"category":93,"check":125,"severity":23,"summary":126},"Runtime stability","The skill relies on standard command-line tools and APIs, making it portable across POSIX-compliant environments.",{"category":44,"check":128,"severity":23,"summary":129},"README","The README file is comprehensive, clearly explains the purpose of the skills in the repository, and details installation and usage.",{"category":32,"check":131,"severity":41,"summary":132},"Tool surface size","This is a plugin with a single core function, not a collection of multiple tools.",{"category":39,"check":134,"severity":41,"summary":135},"Overlapping near-synonym tools","The plugin itself does not expose multiple tools with overlapping functionalities; its core function is singular.",{"category":44,"check":137,"severity":23,"summary":138},"Phantom features","All features described in the README, such as querying leaderboards and filtering by device, have corresponding implementations in the skill script.",{"category":140,"check":141,"severity":23,"summary":142},"Install","Installation instruction","The README provides clear, copy-pasteable installation instructions for various agents, including Claude Code, Codex, Gemini CLI, and Cursor.",{"category":144,"check":145,"severity":67,"summary":146},"Errors","Actionable error messages","Error messages for API call failures are present but lack specific remediation steps or links to documentation, making them less actionable for the agent.",{"category":148,"check":149,"severity":41,"summary":150},"Execution","Pinned dependencies","The skill relies on system-installed tools like `curl` and `jq`, not bundled third-party dependencies that would require pinning.",{"category":32,"check":152,"severity":41,"summary":153},"Dry-run preview","The skill is read-only and does not perform any state-changing operations or outbound data sending, so a dry-run mode is not applicable.",{"category":155,"check":156,"severity":41,"summary":157},"Protocol","Idempotent retry & timeouts","The skill performs read-only API queries and does not involve state-changing operations or remote calls that would require idempotency or timeouts.",{"category":118,"check":159,"severity":41,"summary":160},"Telemetry opt-in","The skill script does not appear to emit any telemetry data.",{"category":39,"check":162,"severity":41,"summary":163},"Name collisions","This is a single plugin with a defined name, so there are no internal name collisions to evaluate.",{"category":39,"check":165,"severity":41,"summary":166},"Hooks-off mechanism","This plugin does not appear to utilize hooks that would require a hooks-off mechanism.",{"category":39,"check":168,"severity":41,"summary":169},"Hook matcher tightness","The plugin does not use hooks, so hook matcher tightness is not applicable.",{"category":65,"check":171,"severity":41,"summary":172},"Hook security","There are no hooks present in this plugin, making hook security irrelevant.",{"category":89,"check":174,"severity":41,"summary":175},"Silent prompt rewriting","The plugin does not have any UserPromptSubmit hooks, so silent prompt rewriting is not applicable.",{"category":65,"check":177,"severity":41,"summary":178},"Permission Hook","The plugin does not implement any PermissionRequest hooks.",{"category":118,"check":180,"severity":41,"summary":181},"Hook privacy","This plugin does not utilize hooks, so hook privacy is not applicable.",{"category":105,"check":183,"severity":41,"summary":184},"Hook dependency","No hooks are present in this plugin.",{"category":44,"check":186,"severity":23,"summary":187},"Feature Transparency","The README clearly explains the functionality and the README's `huggingface-best` skill description aligns with the SKILL.md content.",{"category":189,"check":190,"severity":23,"summary":191},"Convention","Layout convention adherence","The repository structure follows standard conventions, with the skill logic in `skills/huggingface-best/SKILL.md` and general metadata at the root.",{"category":189,"check":193,"severity":41,"summary":194},"Plugin state","The plugin does not appear to manage persistent state that would require ${CLAUDE_PLUGIN_DATA}.",{"category":65,"check":196,"severity":67,"summary":197},"Keychain-stored secrets","The skill references a Hugging Face token from `~/.cache/huggingface/token`, which is likely stored in plain text and not routed through secure keychain storage.",{"category":199,"check":200,"severity":23,"summary":201},"Installation","Clean uninstall","The plugin does not install background daemons, cron jobs, or other persistent services, ensuring a clean uninstall.",1778690942463,"This plugin queries Hugging Face leaderboards and benchmarks to recommend AI models based on task type and hardware constraints. It fetches model metadata, filters by size, and presents a comparison table.",[205,206,207,208,209],"Query Hugging Face leaderboards and benchmarks","Recommend top models based on task type","Filter models by hardware constraints (e.g., device memory)","Enrich model data with parameter count and license information","Present model comparisons in a tabular format",[211,212,213,214],"Training or fine-tuning AI models.","Running AI models directly.","Providing an exhaustive list of all available models.","Evaluating models not present on official Hugging Face leaderboards.","3.0.0","4.4.0","To help users find the most suitable AI model for their specific tasks and hardware limitations by leveraging Hugging Face's benchmark data.","The plugin has a clear purpose, good documentation, and recent commits, but the handling of Hugging Face API tokens (Secret Management, Keychain-stored secrets) and the lack of input validation (Validation, Actionable error messages) warrant a warning. The Secret Management check, specifically the direct reference to `~/.cache/huggingface/token`, is the primary concern driving the score.",79,"A well-documented and functional plugin for recommending AI models, with minor concerns around token handling and validation.",[222,223,224,225,226,227],"huggingface","ai-model-recommendation","leaderboards","benchmarks","cli","developer-tools","global","community",[231,232,233,234],"When seeking the best AI model for a specific task like coding or text generation.","When comparing different AI models based on benchmark scores and size.","When unsure which AI model can run on available hardware.","When needing to quickly identify state-of-the-art models for a given use case.",{"codeQuality":236,"collectedAt":238,"documentation":239,"maintenance":242,"security":248,"testCoverage":250},{"hasLockfile":237},false,1778690921270,{"descriptionLength":240,"readmeSize":241},175,9821,{"closedIssues90d":243,"forks":244,"hasChangelog":237,"openIssues90d":245,"pushedAt":246,"stars":247},6,663,4,1778593131000,10482,{"hasNpmPackage":237,"license":249,"smitheryVerified":237},"Apache-2.0",{"hasCi":251,"hasTests":237},true,{"updatedAt":253},1778690943353,{"basePath":255,"githubOwner":222,"githubRepo":256,"locale":17,"slug":12,"type":257},"skills/huggingface-best","skills","plugin",{"_creationTime":259,"_id":260,"community":261,"display":262,"identity":267,"parentExtension":270,"providers":271,"relations":287,"tags":289,"workflow":290},1778690773482.4824,"k17es3r8wd37t5rrwqcpp5kwrh86mxx8",{"reviewCount":8},{"description":263,"installMethods":264,"name":266,"sourceUrl":13},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":265},"huggingface/skills","huggingface-skills",{"basePath":268,"githubOwner":222,"githubRepo":256,"locale":17,"slug":256,"type":269},"","marketplace",null,{"evaluate":272,"extract":281},{"promptVersionExtension":273,"promptVersionScoring":216,"score":274,"tags":275,"targetMarket":228,"tier":280},"3.1.0",95,[276,222,277,278,279,227],"ai-ml","datasets","models","research","verified",{"commitSha":282,"marketplace":283,"plugin":285},"HEAD",{"name":266,"pluginCount":284},14,{"mcpCount":8,"provider":286,"skillCount":8},"classify",{"repoId":288},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[276,277,227,222,278,279],{"evaluatedAt":291,"extractAt":292,"updatedAt":291},1778690814090,1778690773482,{"evaluate":294,"extract":296},{"promptVersionExtension":215,"promptVersionScoring":216,"score":219,"tags":295,"targetMarket":228,"tier":229},[222,223,224,225,226,227],{"commitSha":282},{"parentExtensionId":260,"repoId":288},{"_creationTime":299,"_id":288,"identity":300,"providers":301,"workflow":740},1778689536128.5474,{"githubOwner":222,"githubRepo":256,"sourceUrl":13},{"classify":302,"discover":733,"github":736},{"commitSha":282,"extensions":303},[304,317,326,334,342,350,358,363,371,379,387,395,403,411,419,427,470,479,485,491,508,514,521,563,574,593,599,619,631,655,713],{"basePath":268,"description":263,"displayName":266,"installMethods":305,"rationale":306,"selectedPaths":307,"source":316,"sourceLanguage":17,"type":269},{"claudeCode":265},"marketplace.json at .claude-plugin/marketplace.json",[308,311,313],{"path":309,"priority":310},".claude-plugin/marketplace.json","mandatory",{"path":312,"priority":310},"README.md",{"path":314,"priority":315},"LICENSE","high","rule",{"basePath":318,"description":319,"displayName":320,"installMethods":321,"rationale":322,"selectedPaths":323,"source":316,"sourceLanguage":17,"type":257},"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":320},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[324],{"path":325,"priority":315},"SKILL.md",{"basePath":327,"description":328,"displayName":329,"installMethods":330,"rationale":331,"selectedPaths":332,"source":316,"sourceLanguage":17,"type":257},"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":329},"inline plugin source from marketplace.json at skills/huggingface-local-models",[333],{"path":325,"priority":315},{"basePath":335,"description":336,"displayName":337,"installMethods":338,"rationale":339,"selectedPaths":340,"source":316,"sourceLanguage":17,"type":257},"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":337},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[341],{"path":325,"priority":315},{"basePath":343,"description":344,"displayName":345,"installMethods":346,"rationale":347,"selectedPaths":348,"source":316,"sourceLanguage":17,"type":257},"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":345},"inline plugin source from marketplace.json at skills/huggingface-papers",[349],{"path":325,"priority":315},{"basePath":351,"description":352,"displayName":353,"installMethods":354,"rationale":355,"selectedPaths":356,"source":316,"sourceLanguage":17,"type":257},"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":353},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[357],{"path":325,"priority":315},{"basePath":255,"description":10,"displayName":12,"installMethods":359,"rationale":360,"selectedPaths":361,"source":316,"sourceLanguage":17,"type":257},{"claudeCode":12},"inline plugin source from marketplace.json at skills/huggingface-best",[362],{"path":325,"priority":315},{"basePath":364,"description":365,"displayName":366,"installMethods":367,"rationale":368,"selectedPaths":369,"source":316,"sourceLanguage":17,"type":257},"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":366},"inline plugin source from marketplace.json at skills/hf-cli",[370],{"path":325,"priority":315},{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":316,"sourceLanguage":17,"type":257},"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":374},"inline plugin source from marketplace.json at skills/huggingface-trackio",[378],{"path":325,"priority":315},{"basePath":380,"description":381,"displayName":382,"installMethods":383,"rationale":384,"selectedPaths":385,"source":316,"sourceLanguage":17,"type":257},"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":382},"inline plugin source from marketplace.json at skills/huggingface-datasets",[386],{"path":325,"priority":315},{"basePath":388,"description":389,"displayName":390,"installMethods":391,"rationale":392,"selectedPaths":393,"source":316,"sourceLanguage":17,"type":257},"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":390},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[394],{"path":325,"priority":315},{"basePath":396,"description":397,"displayName":398,"installMethods":399,"rationale":400,"selectedPaths":401,"source":316,"sourceLanguage":17,"type":257},"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":398},"inline plugin source from marketplace.json at skills/huggingface-gradio",[402],{"path":325,"priority":315},{"basePath":404,"description":405,"displayName":406,"installMethods":407,"rationale":408,"selectedPaths":409,"source":316,"sourceLanguage":17,"type":257},"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":406},"inline plugin source from marketplace.json at skills/transformers-js",[410],{"path":325,"priority":315},{"basePath":412,"description":413,"displayName":414,"installMethods":415,"rationale":416,"selectedPaths":417,"source":316,"sourceLanguage":17,"type":257},"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":414},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[418],{"path":325,"priority":315},{"basePath":420,"description":421,"displayName":422,"installMethods":423,"rationale":424,"selectedPaths":425,"source":316,"sourceLanguage":17,"type":257},"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":422},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[426],{"path":325,"priority":315},{"basePath":268,"description":263,"displayName":266,"installMethods":428,"license":249,"rationale":429,"selectedPaths":430,"source":316,"sourceLanguage":17,"type":257},{"claudeCode":266},"plugin manifest at .claude-plugin/plugin.json",[431,433,434,435,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468],{"path":432,"priority":310},".claude-plugin/plugin.json",{"path":312,"priority":310},{"path":314,"priority":315},{"path":436,"priority":437},"skills/hf-cli/SKILL.md","medium",{"path":439,"priority":437},"skills/huggingface-best/SKILL.md",{"path":441,"priority":437},"skills/huggingface-community-evals/SKILL.md",{"path":443,"priority":437},"skills/huggingface-datasets/SKILL.md",{"path":445,"priority":437},"skills/huggingface-gradio/SKILL.md",{"path":447,"priority":437},"skills/huggingface-llm-trainer/SKILL.md",{"path":449,"priority":437},"skills/huggingface-local-models/SKILL.md",{"path":451,"priority":437},"skills/huggingface-paper-publisher/SKILL.md",{"path":453,"priority":437},"skills/huggingface-papers/SKILL.md",{"path":455,"priority":437},"skills/huggingface-tool-builder/SKILL.md",{"path":457,"priority":437},"skills/huggingface-trackio/SKILL.md",{"path":459,"priority":437},"skills/huggingface-vision-trainer/SKILL.md",{"path":461,"priority":437},"skills/train-sentence-transformers/SKILL.md",{"path":463,"priority":437},"skills/transformers-js/SKILL.md",{"path":465,"priority":310},".mcp.json",{"path":467,"priority":315},"agents/AGENTS.md",{"path":469,"priority":315},".cursor-plugin/plugin.json",{"basePath":471,"description":472,"displayName":473,"installMethods":474,"rationale":475,"selectedPaths":476,"source":316,"sourceLanguage":17,"type":478},"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":265},"SKILL.md frontmatter at hf-mcp/skills/hf-mcp/SKILL.md",[477],{"path":325,"priority":310},"skill",{"basePath":364,"description":480,"displayName":366,"installMethods":481,"rationale":482,"selectedPaths":483,"source":316,"sourceLanguage":17,"type":478},"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":265},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[484],{"path":325,"priority":310},{"basePath":255,"description":486,"displayName":12,"installMethods":487,"rationale":488,"selectedPaths":489,"source":316,"sourceLanguage":17,"type":478},"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":265},"SKILL.md frontmatter at skills/huggingface-best/SKILL.md",[490],{"path":325,"priority":310},{"basePath":351,"description":492,"displayName":353,"installMethods":493,"rationale":494,"selectedPaths":495,"source":316,"sourceLanguage":17,"type":478},"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. 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