[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-plugin-huggingface-huggingface-trackio-zh-CN":3,"guides-for-huggingface-huggingface-trackio":747,"similar-k1785s3263hzxs6ne1nsbzas5186m86v-zh-CN":748},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":14,"identity":255,"isFallback":252,"parentExtension":259,"providers":294,"relations":298,"repo":299,"tags":745,"workflow":746},1778690773482.4844,"k1785s3263hzxs6ne1nsbzas5186m86v",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":12,"sourceUrl":13},"Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces.",{"claudeCode":12},"huggingface-trackio","https://github.com/huggingface/skills",{"_creationTime":15,"_id":16,"extensionId":5,"locale":17,"result":18,"trustSignals":236,"workflow":253},1778690988981.6604,"kn7efrf230dw68p6wqmq883fds86mv29","en",{"checks":19,"evaluatedAt":204,"extensionSummary":205,"features":206,"nonGoals":212,"promptVersionExtension":216,"promptVersionScoring":217,"purpose":218,"rationale":219,"score":220,"summary":221,"tags":222,"targetMarket":229,"tier":230,"useCases":231},[20,25,28,31,35,38,42,46,49,52,56,60,64,68,71,74,77,80,83,86,90,94,98,102,106,109,112,115,119,122,125,128,131,134,137,141,145,149,152,156,159,162,165,168,171,174,177,180,183,186,190,193,196,200],{"category":21,"check":22,"severity":23,"summary":24},"Practical Utility","Problem relevance","pass","The description clearly states that the extension tracks and visualizes ML training experiments, addressing the problem of monitoring and analyzing training runs.",{"category":21,"check":26,"severity":23,"summary":27},"Unique selling proposition","The extension offers a specialized API and CLI for ML experiment tracking, syncing to HF Spaces for real-time dashboards, which goes beyond basic LLM capabilities.",{"category":21,"check":29,"severity":23,"summary":30},"Production readiness","The plugin provides a complete lifecycle for ML experiment tracking, including Python API for logging/alerts and CLI for retrieval and visualization, suitable for real-world workflows.",{"category":32,"check":33,"severity":23,"summary":34},"Scope","Single responsibility principle","The extension focuses solely on tracking and visualizing ML training experiments, without venturing into unrelated domains.",{"category":32,"check":36,"severity":23,"summary":37},"Description quality","The displayed description accurately reflects the extension's functionality, which is to track and visualize ML training experiments with a Python API and CLI.",{"category":39,"check":40,"severity":23,"summary":41},"Invocation","Scoped tools","The extension exposes specific tools like `trackio.init`, `trackio.log`, `trackio.alert`, and CLI commands for listing and retrieving metrics, all focused on experiment tracking.",{"category":43,"check":44,"severity":23,"summary":45},"Documentation","Configuration & parameter reference","The SKILL.md provides detailed references for logging metrics, alerts, and retrieving metrics via CLI, including setup and configuration options.",{"category":32,"check":47,"severity":23,"summary":48},"Tool naming","Tools and commands like `trackio.init`, `trackio.log`, `trackio.alert`, and `trackio list` are descriptive and relevant to experiment tracking.",{"category":32,"check":50,"severity":23,"summary":51},"Minimal I/O surface","Inputs for the Python API and CLI are structured and specific to experiment tracking parameters, and outputs are focused on metrics and alerts.",{"category":53,"check":54,"severity":23,"summary":55},"License","License usability","The extension is licensed under the Apache-2.0 license, which is a permissive open-source license.",{"category":57,"check":58,"severity":23,"summary":59},"Maintenance","Commit recency","The last commit was on 2026-05-12, which is recent and indicates active maintenance.",{"category":57,"check":61,"severity":62,"summary":63},"Dependency Management","not_applicable","No third-party dependencies were detected that require specific management measures.",{"category":65,"check":66,"severity":23,"summary":67},"Security","Secret Management","The extension uses Hugging Face Hub authentication via API keys or tokens, which are handled through environment variables or configuration and not echoed.",{"category":65,"check":69,"severity":23,"summary":70},"Injection","The extension loads data via Python API and CLI commands, and its SKILL.md does not reference any external content that could be used for injection.",{"category":65,"check":72,"severity":23,"summary":73},"Transitive Supply-Chain Grenades","All code and scripts are bundled within the repository, and there are no runtime downloads or remote execution patterns observed.",{"category":65,"check":75,"severity":23,"summary":76},"Sandbox Isolation","The extension operates within its defined scope, interacting with Hugging Face Hub and local Python/CLI environments without modifying external files or paths.",{"category":65,"check":78,"severity":23,"summary":79},"Sandbox escape primitives","No detached processes or retry loops around denied tool calls were found in the provided scripts.",{"category":65,"check":81,"severity":23,"summary":82},"Data Exfiltration","The extension collects training metrics and alert data, but this is intended for logging and visualization and not sensitive personal data submission.",{"category":65,"check":84,"severity":23,"summary":85},"Hidden Text Tricks","The bundled content and descriptions do not contain hidden steering tricks, invisible characters, or other obfuscation methods.",{"category":87,"check":88,"severity":23,"summary":89},"Hooks","Opaque code execution","The extension's code is provided as readable Python scripts and bash commands, with no obfuscation or base64 payloads.",{"category":91,"check":92,"severity":23,"summary":93},"Portability","Structural Assumption","The extension makes no structural assumptions about the user's project organization outside of the Python environment for the API and standard CLI usage.",{"category":95,"check":96,"severity":23,"summary":97},"Trust","Issues Attention","Open issues: 4, Closed issues (90d): 6. Closure rate is >50%, indicating good maintainer engagement.",{"category":99,"check":100,"severity":23,"summary":101},"Versioning","Release Management","The `pushedAt` timestamp indicates recent activity, and the Apache-2.0 license implies a stable release model. While no explicit version number is visible, recent commits suggest active development.",{"category":103,"check":104,"severity":23,"summary":105},"Code Execution","Validation","The Python API and CLI commands are structured and likely validated internally by libraries like `trackio` itself, and inputs are constrained by their expected types and formats.",{"category":65,"check":107,"severity":23,"summary":108},"Unguarded Destructive Operations","The extension is primarily for logging and visualization and does not perform destructive operations.",{"category":103,"check":110,"severity":23,"summary":111},"Error Handling","The SKILL.md implies structured error reporting and handling through its API and CLI documentation, with clear messages for retrieval and configuration.",{"category":103,"check":113,"severity":23,"summary":114},"Logging","The core function of the extension is logging metrics, and its output is intended for review and analysis.",{"category":116,"check":117,"severity":23,"summary":118},"Compliance","GDPR","The extension deals with ML training metrics, which are not typically personal data. No PII submission is indicated.",{"category":116,"check":120,"severity":23,"summary":121},"Target market","The extension is globally applicable for ML training and does not have regional or jurisdictional restrictions.",{"category":91,"check":123,"severity":23,"summary":124},"Runtime stability","The extension uses standard Python and CLI tools, making it platform-agnostic for POSIX-like environments and standard Python execution.",{"category":43,"check":126,"severity":23,"summary":127},"README","The README clearly describes the purpose of Hugging Face Skills and provides installation instructions and an overview of available skills.",{"category":32,"check":129,"severity":23,"summary":130},"Tool surface size","The extension exposes a focused set of tools and commands related to experiment tracking, well within the recommended range.",{"category":39,"check":132,"severity":23,"summary":133},"Overlapping near-synonym tools","The exposed tools and commands have distinct functions related to logging, alerting, and retrieving metrics, with no direct synonyms.",{"category":43,"check":135,"severity":23,"summary":136},"Phantom features","All advertised features (Python API for logging/alerts, CLI for retrieval, HF Space sync) are implemented and described in the SKILL.md.",{"category":138,"check":139,"severity":23,"summary":140},"Install","Installation instruction","The README provides clear Claude Code and other agent installation instructions, including copy-paste examples for registering the marketplace and installing skills.",{"category":142,"check":143,"severity":23,"summary":144},"Errors","Actionable error messages","The extension's documentation implies actionable error messages for configuration, retrieval, and API usage, guiding users on remediation.",{"category":146,"check":147,"severity":62,"summary":148},"Execution","Pinned dependencies","The extension does not appear to bundle or manage third-party dependencies directly; it relies on the environment's Python installation.",{"category":32,"check":150,"severity":62,"summary":151},"Dry-run preview","The extension focuses on logging and data retrieval, not state-changing operations that would require a dry-run mode.",{"category":153,"check":154,"severity":23,"summary":155},"Protocol","Idempotent retry & timeouts","The extension's operations are primarily logging and retrieval, which are inherently idempotent or do not require retries with timeouts.",{"category":116,"check":157,"severity":23,"summary":158},"Telemetry opt-in","The extension's telemetry is opt-in by nature (user-initiated logging), and no background telemetry collection is evident.",{"category":39,"check":160,"severity":23,"summary":161},"Name collisions","The `huggingface-trackio` skill name is distinct and does not appear to collide with built-in commands or other bundled skills.",{"category":39,"check":163,"severity":62,"summary":164},"Hooks-off mechanism","This extension is a skill, not a plugin, and thus does not have hooks that require a hooks-off mechanism.",{"category":39,"check":166,"severity":62,"summary":167},"Hook matcher tightness","This extension is a skill, not a plugin, and does not utilize hooks with matchers.",{"category":65,"check":169,"severity":62,"summary":170},"Hook security","This extension is a skill, not a plugin, and does not employ hooks with security implications.",{"category":87,"check":172,"severity":62,"summary":173},"Silent prompt rewriting","This extension is a skill, not a plugin, and does not have hooks that rewrite prompts.",{"category":65,"check":175,"severity":62,"summary":176},"Permission Hook","This extension is a skill, not a plugin, and does not utilize permission hooks.",{"category":116,"check":178,"severity":62,"summary":179},"Hook privacy","This extension is a skill, not a plugin, and does not have hooks that send data over the network.",{"category":103,"check":181,"severity":62,"summary":182},"Hook dependency","This extension is a skill, not a plugin, and does not have script dependencies for hooks.",{"category":43,"check":184,"severity":23,"summary":185},"Feature Transparency","The SKILL.md clearly describes the functionality of the Trackio extension, including its Python API and CLI interfaces.",{"category":187,"check":188,"severity":62,"summary":189},"Convention","Layout convention adherence","This extension is a skill, not a Claude Code plugin with specific layout conventions for `.claude-plugin/` or `bin/` directories.",{"category":187,"check":191,"severity":62,"summary":192},"Plugin state","This extension is a skill, not a plugin, and does not manage persistent state under `${CLAUDE_PLUGIN_DATA}`.",{"category":65,"check":194,"severity":62,"summary":195},"Keychain-stored secrets","This extension is a skill and does not manage secrets that would require keychain storage.",{"category":197,"check":198,"severity":62,"summary":199},"Dependencies","Tagged release sourcing","This extension is a skill and does not bundle MCP servers from external sources.",{"category":201,"check":202,"severity":23,"summary":203},"Installation","Clean uninstall","The extension, being a skill, is installed and uninstalled through the agent's plugin manager and does not spawn background daemons or persistent processes.",1778690988557,"This extension provides the Trackio library for logging ML training metrics via a Python API and retrieving them via a CLI. It supports real-time dashboards synced to Hugging Face Spaces and includes an alerting mechanism for diagnostics.",[207,208,209,210,211],"Log ML training metrics via Python API","Visualize metrics in real-time on HF Spaces dashboards","Fire alerts for training diagnostics","Retrieve metrics and alerts via CLI","Support for automated experiment workflows",[213,214,215],"Replacing full experiment management platforms","Providing a UI for model training itself","Automating model deployment","3.0.0","4.4.0","To enable users to effectively track, visualize, and analyze their machine learning training experiments, facilitating better experiment iteration and monitoring.","The extension demonstrates excellent documentation, clear functionality, and robust security practices. No critical or warning findings were identified.",99,"High-quality plugin for tracking and visualizing ML training experiments.",[223,224,225,226,227,228],"mlops","experiment-tracking","monitoring","huggingface","python","cli","global","verified",[232,233,234,235],"Tracking hyperparameters and performance metrics during model training","Monitoring training progress and identifying issues with real-time dashboards","Setting up alerts for critical events like loss divergence or NaN gradients","Analyzing experiment results programmatically using CLI output",{"codeQuality":237,"collectedAt":239,"documentation":240,"maintenance":243,"security":249,"testCoverage":251},{"hasLockfile":238},false,1778690967917,{"descriptionLength":241,"readmeSize":242},162,9821,{"closedIssues90d":244,"forks":245,"hasChangelog":238,"openIssues90d":246,"pushedAt":247,"stars":248},6,663,4,1778593131000,10482,{"hasNpmPackage":238,"license":250,"smitheryVerified":238},"Apache-2.0",{"hasCi":252,"hasTests":238},true,{"updatedAt":254},1778690988981,{"basePath":256,"githubOwner":226,"githubRepo":257,"locale":17,"slug":12,"type":258},"skills/huggingface-trackio","skills","plugin",{"_creationTime":260,"_id":261,"community":262,"display":263,"identity":268,"parentExtension":271,"providers":272,"relations":288,"tags":290,"workflow":291},1778690773482.4824,"k17es3r8wd37t5rrwqcpp5kwrh86mxx8",{"reviewCount":8},{"description":264,"installMethods":265,"name":267,"sourceUrl":13},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":266},"huggingface/skills","huggingface-skills",{"basePath":269,"githubOwner":226,"githubRepo":257,"locale":17,"slug":257,"type":270},"","marketplace",null,{"evaluate":273,"extract":282},{"promptVersionExtension":274,"promptVersionScoring":217,"score":275,"tags":276,"targetMarket":229,"tier":230},"3.1.0",95,[277,226,278,279,280,281],"ai-ml","datasets","models","research","developer-tools",{"commitSha":283,"marketplace":284,"plugin":286},"HEAD",{"name":267,"pluginCount":285},14,{"mcpCount":8,"provider":287,"skillCount":8},"classify",{"repoId":289},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[277,278,281,226,279,280],{"evaluatedAt":292,"extractAt":293,"updatedAt":292},1778690814090,1778690773482,{"evaluate":295,"extract":297},{"promptVersionExtension":216,"promptVersionScoring":217,"score":220,"tags":296,"targetMarket":229,"tier":230},[223,224,225,226,227,228],{"commitSha":283},{"parentExtensionId":261,"repoId":289},{"_creationTime":300,"_id":289,"identity":301,"providers":302,"workflow":741},1778689536128.5474,{"githubOwner":226,"githubRepo":257,"sourceUrl":13},{"classify":303,"discover":734,"github":737},{"commitSha":283,"extensions":304},[305,318,327,335,343,351,359,367,375,380,388,396,404,412,420,428,471,480,486,492,509,515,522,564,575,594,600,620,632,656,714],{"basePath":269,"description":264,"displayName":267,"installMethods":306,"rationale":307,"selectedPaths":308,"source":317,"sourceLanguage":17,"type":270},{"claudeCode":266},"marketplace.json at .claude-plugin/marketplace.json",[309,312,314],{"path":310,"priority":311},".claude-plugin/marketplace.json","mandatory",{"path":313,"priority":311},"README.md",{"path":315,"priority":316},"LICENSE","high","rule",{"basePath":319,"description":320,"displayName":321,"installMethods":322,"rationale":323,"selectedPaths":324,"source":317,"sourceLanguage":17,"type":258},"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":321},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[325],{"path":326,"priority":316},"SKILL.md",{"basePath":328,"description":329,"displayName":330,"installMethods":331,"rationale":332,"selectedPaths":333,"source":317,"sourceLanguage":17,"type":258},"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":330},"inline plugin source from marketplace.json at skills/huggingface-local-models",[334],{"path":326,"priority":316},{"basePath":336,"description":337,"displayName":338,"installMethods":339,"rationale":340,"selectedPaths":341,"source":317,"sourceLanguage":17,"type":258},"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":338},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[342],{"path":326,"priority":316},{"basePath":344,"description":345,"displayName":346,"installMethods":347,"rationale":348,"selectedPaths":349,"source":317,"sourceLanguage":17,"type":258},"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":346},"inline plugin source from marketplace.json at skills/huggingface-papers",[350],{"path":326,"priority":316},{"basePath":352,"description":353,"displayName":354,"installMethods":355,"rationale":356,"selectedPaths":357,"source":317,"sourceLanguage":17,"type":258},"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":354},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[358],{"path":326,"priority":316},{"basePath":360,"description":361,"displayName":362,"installMethods":363,"rationale":364,"selectedPaths":365,"source":317,"sourceLanguage":17,"type":258},"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":362},"inline plugin source from marketplace.json at skills/huggingface-best",[366],{"path":326,"priority":316},{"basePath":368,"description":369,"displayName":370,"installMethods":371,"rationale":372,"selectedPaths":373,"source":317,"sourceLanguage":17,"type":258},"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":370},"inline plugin source from marketplace.json at skills/hf-cli",[374],{"path":326,"priority":316},{"basePath":256,"description":10,"displayName":12,"installMethods":376,"rationale":377,"selectedPaths":378,"source":317,"sourceLanguage":17,"type":258},{"claudeCode":12},"inline plugin source from marketplace.json at skills/huggingface-trackio",[379],{"path":326,"priority":316},{"basePath":381,"description":382,"displayName":383,"installMethods":384,"rationale":385,"selectedPaths":386,"source":317,"sourceLanguage":17,"type":258},"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":383},"inline plugin source from marketplace.json at skills/huggingface-datasets",[387],{"path":326,"priority":316},{"basePath":389,"description":390,"displayName":391,"installMethods":392,"rationale":393,"selectedPaths":394,"source":317,"sourceLanguage":17,"type":258},"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":391},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[395],{"path":326,"priority":316},{"basePath":397,"description":398,"displayName":399,"installMethods":400,"rationale":401,"selectedPaths":402,"source":317,"sourceLanguage":17,"type":258},"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":399},"inline plugin source from marketplace.json at skills/huggingface-gradio",[403],{"path":326,"priority":316},{"basePath":405,"description":406,"displayName":407,"installMethods":408,"rationale":409,"selectedPaths":410,"source":317,"sourceLanguage":17,"type":258},"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":407},"inline plugin source from marketplace.json at skills/transformers-js",[411],{"path":326,"priority":316},{"basePath":413,"description":414,"displayName":415,"installMethods":416,"rationale":417,"selectedPaths":418,"source":317,"sourceLanguage":17,"type":258},"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":415},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[419],{"path":326,"priority":316},{"basePath":421,"description":422,"displayName":423,"installMethods":424,"rationale":425,"selectedPaths":426,"source":317,"sourceLanguage":17,"type":258},"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":423},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[427],{"path":326,"priority":316},{"basePath":269,"description":264,"displayName":267,"installMethods":429,"license":250,"rationale":430,"selectedPaths":431,"source":317,"sourceLanguage":17,"type":258},{"claudeCode":267},"plugin manifest at .claude-plugin/plugin.json",[432,434,435,436,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469],{"path":433,"priority":311},".claude-plugin/plugin.json",{"path":313,"priority":311},{"path":315,"priority":316},{"path":437,"priority":438},"skills/hf-cli/SKILL.md","medium",{"path":440,"priority":438},"skills/huggingface-best/SKILL.md",{"path":442,"priority":438},"skills/huggingface-community-evals/SKILL.md",{"path":444,"priority":438},"skills/huggingface-datasets/SKILL.md",{"path":446,"priority":438},"skills/huggingface-gradio/SKILL.md",{"path":448,"priority":438},"skills/huggingface-llm-trainer/SKILL.md",{"path":450,"priority":438},"skills/huggingface-local-models/SKILL.md",{"path":452,"priority":438},"skills/huggingface-paper-publisher/SKILL.md",{"path":454,"priority":438},"skills/huggingface-papers/SKILL.md",{"path":456,"priority":438},"skills/huggingface-tool-builder/SKILL.md",{"path":458,"priority":438},"skills/huggingface-trackio/SKILL.md",{"path":460,"priority":438},"skills/huggingface-vision-trainer/SKILL.md",{"path":462,"priority":438},"skills/train-sentence-transformers/SKILL.md",{"path":464,"priority":438},"skills/transformers-js/SKILL.md",{"path":466,"priority":311},".mcp.json",{"path":468,"priority":316},"agents/AGENTS.md",{"path":470,"priority":316},".cursor-plugin/plugin.json",{"basePath":472,"description":473,"displayName":474,"installMethods":475,"rationale":476,"selectedPaths":477,"source":317,"sourceLanguage":17,"type":479},"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":266},"SKILL.md frontmatter at hf-mcp/skills/hf-mcp/SKILL.md",[478],{"path":326,"priority":311},"skill",{"basePath":368,"description":481,"displayName":370,"installMethods":482,"rationale":483,"selectedPaths":484,"source":317,"sourceLanguage":17,"type":479},"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":266},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[485],{"path":326,"priority":311},{"basePath":360,"description":487,"displayName":362,"installMethods":488,"rationale":489,"selectedPaths":490,"source":317,"sourceLanguage":17,"type":479},"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":266},"SKILL.md frontmatter at skills/huggingface-best/SKILL.md",[491],{"path":326,"priority":311},{"basePath":352,"description":493,"displayName":354,"installMethods":494,"rationale":495,"selectedPaths":496,"source":317,"sourceLanguage":17,"type":479},"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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