[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-hf-cli-zh-CN":3,"guides-for-huggingface-hf-cli":733,"similar-k17a3mmgvm5hj49twj487hp64186n2qa-zh-CN":734},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":242,"isFallback":239,"parentExtension":246,"providers":280,"relations":284,"repo":285,"tags":731,"workflow":732},1778690773482.4866,"k17a3mmgvm5hj49twj487hp64186n2qa",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","hf-cli","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":223,"workflow":240},1778691223210.4326,"kn7b88xps60w959frvg7ndbj9d86n5g7","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":204,"promptVersionScoring":205,"purpose":206,"rationale":207,"score":208,"summary":209,"tags":210,"targetMarket":216,"tier":217,"useCases":218},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,114,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly names the problem of managing Hugging Face Hub resources and provides specific use cases and triggers, aligning with the extension's functionality.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","This skill provides a dedicated CLI interface and management capabilities for the Hugging Face Hub ecosystem, offering significant value over generic LLM interactions with Hugging Face services.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension covers the complete lifecycle for managing Hugging Face resources via the CLI, including download, upload, management, and deployment, making it production-ready.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses solely on interacting with the Hugging Face Hub via its CLI, encompassing related resources like models, datasets, and jobs in a coherent domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The description accurately reflects the functionality of the hf CLI, is well-organized, and clearly outlines the scope and use cases.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The CLI exposes numerous narrow verb-noun specialist tools for specific Hugging Face Hub operations, promoting precise agent selection.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md provides detailed command structures with options and arguments for all commands, and the README offers tips on authentication and usage.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","All exposed tool names are descriptive verb-noun commands (e.g., `hf download`, `hf auth login`) within the Hugging Face domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The CLI commands are structured with specific parameters, and the output formats are defined, avoiding excessive or unnecessary data exposure.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the Apache-2.0 license, as indicated by the bundled LICENSE file, which is a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The repository shows recent commit activity within the last 12 months, indicating active maintenance.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","The skill primarily wraps an existing CLI tool and does not appear to manage significant third-party dependencies directly.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","The CLI handles authentication tokens and secrets via environment variables or dedicated auth commands, not by echoing them to stdout.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The CLI commands operate on Hugging Face Hub resources and do not appear to execute arbitrary third-party code or load untrusted instructions.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The CLI tool is installed via a script and subsequent commands interact with the Hugging Face Hub API, not fetching or executing arbitrary remote code.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The CLI tool operates within the scope of the Hugging Face Hub and local cache, not modifying files outside its designated operational areas.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached process spawns or deny-retry loops were detected in the CLI's operational model.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The CLI interacts with the Hugging Face Hub API for managing resources; no undocumented outbound calls or submission of confidential data were observed.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled SKILL.md and README files are free of hidden-steering tricks, relying on standard markdown and text.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The installation script is straightforward, and the CLI itself is compiled code, not an opaque execution payload.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The CLI operates on Hugging Face Hub resources and local cache, making no assumptions about user project structure beyond standard file paths for cache/downloads.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","With 4 issues opened and 6 closed in the last 90 days, the closure rate is well above 50%, indicating active maintainer engagement.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The SKILL.md frontmatter includes a version (`huggingface_hub v1.14.0`) and the CLI has an update command, indicating clear versioning.",{"category":104,"check":105,"severity":24,"summary":106},"Execution","Validation","The CLI commands utilize structured arguments and parameters, implying internal validation and sanitization of inputs.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","Destructive operations like deleting repos or buckets require explicit commands and user confirmation where applicable (e.g., `--yes` flag), preventing silent execution.",{"category":111,"check":112,"severity":24,"summary":113},"Code Execution","Error Handling","The CLI provides structured error messages for various operations, and the installation script exits non-zero on failure.",{"category":111,"check":115,"severity":24,"summary":116},"Logging","The CLI offers a `--verbose` flag for detailed output and the installation script provides standard logging, though a dedicated audit log isn't explicitly mentioned.",{"category":118,"check":119,"severity":63,"summary":120},"Compliance","GDPR","The extension primarily interacts with Hugging Face Hub resources and does not inherently operate on personal data without explicit user action or submission.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The Hugging Face Hub CLI is globally accessible and usable without any regional restrictions.",{"category":92,"check":125,"severity":24,"summary":126},"Runtime stability","The CLI is installed via a cross-platform script and is designed to run on various operating systems, providing a stable runtime.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README file is comprehensive, detailing installation, usage across different agents, and contribution guidelines.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The CLI exposes a significant number of commands, but they are well-organized into subcommands, keeping the surface manageable and focused.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","While there are many commands, they are distinct and organized by function (e.g., `hf models list`, `hf datasets list`), avoiding direct near-synonyms for similar actions.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All features advertised in the README and SKILL.md, such as downloading, uploading, and managing resources, are directly supported by the CLI commands.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","Clear, copy-pasteable installation instructions for various agents are provided in the README, along with authentication guidance.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The CLI provides descriptive error messages, and the installation script guides users on remediation or provides `--help` context.",{"category":104,"check":148,"severity":24,"summary":149},"Pinned dependencies","The installation script installs the `hf` CLI tool, which manages its own dependencies, and the `SKILL.md` references a specific version.",{"category":33,"check":151,"severity":24,"summary":152},"Dry-run preview","Several commands, like `hf download` and `hf sync`, include a `--dry-run` flag to preview actions without executing them.",{"category":154,"check":155,"severity":63,"summary":156},"Protocol","Idempotent retry & timeouts","The CLI interacts with the Hugging Face Hub API; idempotency and timeouts are handled by the underlying API and not directly exposed as a tool-level concern.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The CLI tool's telemetry behavior is not explicitly detailed, but it is a standard CLI tool and not an agent skill that would typically require opt-in telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The description clearly defines the artifact (Hugging Face Hub resources) and the user intent (downloading, uploading, managing), with specific triggers and boundaries.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and self-contained, clearly stating the core capability of the hf CLI and relevant trigger phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is well-structured with commands and sections, keeping the core instructions concise and delegating details to the CLI's help output.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the main commands, and detailed options/examples are implicitly available via `hf \u003Ccommand> --help`, following a progressive disclosure pattern.",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","This skill is a direct CLI wrapper and does not involve deep exploration or code review that would necessitate context forking.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The README and CLI help provide numerous ready-to-use examples for various commands, covering common use cases.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The CLI's `--help` output details numerous options and flags that allow for handling various scenarios and edge cases, and the SKILL.md mentions authentication and update commands.",{"category":111,"check":183,"severity":63,"summary":184},"Tool Fallback","This skill is a direct CLI wrapper and does not rely on external MCP servers or other skills as optional fallbacks.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The CLI commands would naturally halt if pre-conditions like authentication or repository existence are not met, reporting errors to the user.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The `hf-cli` skill operates as a standalone tool and does not implicitly rely on other skills being loaded in the same session.",1778691222995,"This skill provides access to the Hugging Face Hub CLI (`hf`), enabling users to download, upload, and manage models, datasets, spaces, buckets, and jobs. It supports authentication, environment configuration, and integrates with various Hugging Face services.",[195,196,197,198,199],"Download and upload models and datasets","Manage Hugging Face repositories and buckets","Run and schedule jobs on Hugging Face infrastructure","Handle authentication and manage local cache","Deploy and manage Hugging Face Inference Endpoints",[201,202,203],"Replacing the need for the Hugging Face Hub platform itself.","Providing a GUI for Hugging Face Hub operations.","Managing resources on cloud providers other than Hugging Face infrastructure.","3.0.0","4.4.0","To provide a powerful and versatile command-line interface for interacting with the Hugging Face Hub, streamlining ML workflows from data management to model deployment.","The extension is a well-maintained, production-ready CLI wrapper for the Hugging Face Hub with comprehensive documentation, clear commands, and robust error handling, earning a verified tier.",100,"A comprehensive and well-maintained CLI for managing all aspects of the Hugging Face Hub.",[211,212,213,214,215],"cli","huggingface","mlops","data-management","model-management","global","verified",[219,220,221,222],"Use when handling authentication for Hugging Face Hub.","Use when managing local cache for models and datasets.","Use when deploying and managing HF Inference Endpoints.","Use when needing to interact with any part of the Hugging Face ecosystem.",{"codeQuality":224,"collectedAt":226,"documentation":227,"maintenance":230,"security":236,"testCoverage":238},{"hasLockfile":225},false,1778691206143,{"descriptionLength":228,"readmeSize":229},1008,9821,{"closedIssues90d":231,"forks":232,"hasChangelog":225,"openIssues90d":233,"pushedAt":234,"stars":235},6,663,4,1778593131000,10482,{"hasNpmPackage":225,"license":237,"smitheryVerified":225},"Apache-2.0",{"hasCi":239,"hasTests":225},true,{"updatedAt":241},1778691223210,{"basePath":243,"githubOwner":212,"githubRepo":244,"locale":18,"slug":13,"type":245},"skills/hf-cli","skills","skill",{"_creationTime":247,"_id":248,"community":249,"display":250,"identity":255,"parentExtension":258,"providers":259,"relations":274,"tags":276,"workflow":277},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":251,"installMethods":252,"name":254,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":253},"huggingface-skills","Hugging Face Skills",{"basePath":256,"githubOwner":212,"githubRepo":244,"locale":18,"slug":244,"type":257},"","plugin",null,{"evaluate":260,"extract":269},{"promptVersionExtension":204,"promptVersionScoring":205,"score":261,"tags":262,"targetMarket":216,"tier":217},98,[212,263,264,265,266,267,211,268],"ai","ml","datasets","models","training","python",{"commitSha":270,"license":237,"plugin":271},"HEAD",{"mcpCount":8,"provider":272,"skillCount":273},"classify",14,{"repoId":275},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[263,211,265,212,264,266,268,267],{"evaluatedAt":278,"extractAt":279,"updatedAt":278},1778691185872,1778690773482,{"evaluate":281,"extract":283},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":282,"targetMarket":216,"tier":217},[211,212,213,214,215],{"commitSha":270},{"parentExtensionId":248,"repoId":275},{"_creationTime":286,"_id":275,"identity":287,"providers":288,"workflow":727},1778689536128.5474,{"githubOwner":212,"githubRepo":244,"sourceUrl":14},{"classify":289,"discover":720,"github":723},{"commitSha":270,"extensions":290},[291,305,314,322,330,338,346,354,360,368,376,384,392,400,408,416,459,467,472,478,495,501,508,550,561,580,586,606,618,642,700],{"basePath":256,"description":251,"displayName":253,"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":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":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":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":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":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":325},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[329],{"path":313,"priority":302},{"basePath":331,"description":332,"displayName":333,"installMethods":334,"rationale":335,"selectedPaths":336,"source":303,"sourceLanguage":18,"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":333},"inline plugin source from marketplace.json at skills/huggingface-papers",[337],{"path":313,"priority":302},{"basePath":339,"description":340,"displayName":341,"installMethods":342,"rationale":343,"selectedPaths":344,"source":303,"sourceLanguage":18,"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":341},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[345],{"path":313,"priority":302},{"basePath":347,"description":348,"displayName":349,"installMethods":350,"rationale":351,"selectedPaths":352,"source":303,"sourceLanguage":18,"type":257},"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":349},"inline plugin source from marketplace.json at skills/huggingface-best",[353],{"path":313,"priority":302},{"basePath":243,"description":355,"displayName":13,"installMethods":356,"rationale":357,"selectedPaths":358,"source":303,"sourceLanguage":18,"type":257},"Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.",{"claudeCode":13},"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":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":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":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":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":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":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":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":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":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":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":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":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":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":411},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[415],{"path":313,"priority":302},{"basePath":256,"description":251,"displayName":253,"installMethods":417,"license":237,"rationale":418,"selectedPaths":419,"source":303,"sourceLanguage":18,"type":257},{"claudeCode":253},"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":245},"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":243,"description":10,"displayName":13,"installMethods":468,"rationale":469,"selectedPaths":470,"source":303,"sourceLanguage":18,"type":245},{"claudeCode":12},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[471],{"path":313,"priority":297},{"basePath":347,"description":473,"displayName":349,"installMethods":474,"rationale":475,"selectedPaths":476,"source":303,"sourceLanguage":18,"type":245},"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",[477],{"path":313,"priority":297},{"basePath":339,"description":479,"displayName":341,"installMethods":480,"rationale":481,"selectedPaths":482,"source":303,"sourceLanguage":18,"type":245},"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",[483,484,487,489,491,493],{"path":313,"priority":297},{"path":485,"priority":486},"examples/.env.example","low",{"path":488,"priority":486},"examples/USAGE_EXAMPLES.md",{"path":490,"priority":486},"scripts/inspect_eval_uv.py",{"path":492,"priority":486},"scripts/inspect_vllm_uv.py",{"path":494,"priority":486},"scripts/lighteval_vllm_uv.py",{"basePath":369,"description":496,"displayName":371,"installMethods":497,"rationale":498,"selectedPaths":499,"source":303,"sourceLanguage":18,"type":245},"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",[500],{"path":313,"priority":297},{"basePath":385,"description":386,"displayName":387,"installMethods":502,"rationale":503,"selectedPaths":504,"source":303,"sourceLanguage":18,"type":245},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[505,506],{"path":313,"priority":297},{"path":507,"priority":426},"examples.md",{"basePath":306,"description":509,"displayName":308,"installMethods":510,"rationale":511,"selectedPaths":512,"source":303,"sourceLanguage":18,"type":245},"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",[513,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548],{"path":313,"priority":297},{"path":515,"priority":426},"references/gguf_conversion.md",{"path":517,"priority":426},"references/hardware_guide.md",{"path":519,"priority":426},"references/hub_saving.md",{"path":521,"priority":426},"references/local_training_macos.md",{"path":523,"priority":426},"references/reliability_principles.md",{"path":525,"priority":426},"references/trackio_guide.md",{"path":527,"priority":426},"references/training_methods.md",{"path":529,"priority":426},"references/training_patterns.md",{"path":531,"priority":426},"references/troubleshooting.md",{"path":533,"priority":426},"references/unsloth.md",{"path":535,"priority":486},"scripts/convert_to_gguf.py",{"path":537,"priority":486},"scripts/dataset_inspector.py",{"path":539,"priority":486},"scripts/estimate_cost.py",{"path":541,"priority":486},"scripts/hf_benchmarks.py",{"path":543,"priority":486},"scripts/train_dpo_example.py",{"path":545,"priority":486},"scripts/train_grpo_example.py",{"path":547,"priority":486},"scripts/train_sft_example.py",{"path":549,"priority":486},"scripts/unsloth_sft_example.py",{"basePath":315,"description":316,"displayName":317,"installMethods":551,"rationale":552,"selectedPaths":553,"source":303,"sourceLanguage":18,"type":245},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[554,555,557,559],{"path":313,"priority":297},{"path":556,"priority":426},"references/hardware.md",{"path":558,"priority":426},"references/hub-discovery.md",{"path":560,"priority":426},"references/quantization.md",{"basePath":323,"description":324,"displayName":325,"installMethods":562,"rationale":563,"selectedPaths":564,"source":303,"sourceLanguage":18,"type":245},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[565,566,568,570,572,574,576,578],{"path":313,"priority":297},{"path":567,"priority":486},"examples/example_usage.md",{"path":569,"priority":426},"references/quick_reference.md",{"path":571,"priority":486},"scripts/paper_manager.py",{"path":573,"priority":486},"templates/arxiv.md",{"path":575,"priority":486},"templates/ml-report.md",{"path":577,"priority":486},"templates/modern.md",{"path":579,"priority":486},"templates/standard.md",{"basePath":331,"description":581,"displayName":333,"installMethods":582,"rationale":583,"selectedPaths":584,"source":303,"sourceLanguage":18,"type":245},"Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.",{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-papers/SKILL.md",[585],{"path":313,"priority":297},{"basePath":377,"description":587,"displayName":379,"installMethods":588,"rationale":589,"selectedPaths":590,"source":303,"sourceLanguage":18,"type":245},"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":245},"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":245},"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":519,"priority":426},{"path":628,"priority":426},"references/image_classification_training_notebook.md",{"path":630,"priority":426},"references/object_detection_training_notebook.md",{"path":523,"priority":426},{"path":633,"priority":426},"references/timm_trainer.md",{"path":537,"priority":486},{"path":539,"priority":486},{"path":637,"priority":486},"scripts/image_classification_training.py",{"path":639,"priority":486},"scripts/object_detection_training.py",{"path":641,"priority":486},"scripts/sam_segmentation_training.py",{"basePath":409,"description":643,"displayName":411,"installMethods":644,"rationale":645,"selectedPaths":646,"source":303,"sourceLanguage":18,"type":245},"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":517,"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":531,"priority":426},{"path":675,"priority":486},"scripts/mine_hard_negatives.py",{"path":677,"priority":486},"scripts/train_cross_encoder_distillation_example.py",{"path":679,"priority":486},"scripts/train_cross_encoder_example.py",{"path":681,"priority":486},"scripts/train_cross_encoder_listwise_example.py",{"path":683,"priority":486},"scripts/train_sentence_transformer_distillation_example.py",{"path":685,"priority":486},"scripts/train_sentence_transformer_example.py",{"path":687,"priority":486},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":689,"priority":486},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":691,"priority":486},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":693,"priority":486},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":695,"priority":486},"scripts/train_sentence_transformer_with_lora_example.py",{"path":697,"priority":486},"scripts/train_sparse_encoder_distillation_example.py",{"path":699,"priority":486},"scripts/train_sparse_encoder_example.py",{"basePath":393,"description":701,"displayName":395,"installMethods":702,"rationale":703,"selectedPaths":704,"source":303,"sourceLanguage":18,"type":245},"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":231,"description":724,"forks":232,"homepage":725,"license":237,"openIssues90d":233,"pushedAt":234,"readmeSize":229,"stars":235,"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,[211,214,212,213,215],{"evaluatedAt":241,"extractAt":279,"updatedAt":241},[],[735,761,790,819,850,878],{"_creationTime":736,"_id":737,"community":738,"display":739,"identity":745,"providers":749,"relations":755,"tags":757,"workflow":758},1778687399826.0234,"k1728fhz3xvt5de6ck3kd5gbsh86ngnm",{"reviewCount":8},{"description":740,"installMethods":741,"name":743,"sourceUrl":744},"Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.",{"claudeCode":742},"github/awesome-copilot","arize-dataset","https://github.com/github/awesome-copilot",{"basePath":746,"githubOwner":747,"githubRepo":748,"locale":18,"slug":743,"type":245},"skills/arize-dataset","github","awesome-copilot",{"evaluate":750,"extract":754},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":751,"targetMarket":216,"tier":217},[752,214,211,213,265,753],"arize","examples",{"commitSha":270},{"repoId":756},"kd7dsmv976w8rtkqnjjfdtfgks86nnmw",[752,211,214,265,753,213],{"evaluatedAt":759,"extractAt":760,"updatedAt":759},1778689111827,1778687399826,{"_creationTime":762,"_id":763,"community":764,"display":765,"identity":771,"providers":775,"relations":783,"tags":786,"workflow":787},1778695548458.3362,"k17a1whbhdsh4zzs4k78vmf4e586n2k8",{"reviewCount":8},{"description":766,"installMethods":767,"name":769,"sourceUrl":770},"Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.\n",{"claudeCode":768},"pjt222/agent-almanac","build-feature-store","https://github.com/pjt222/agent-almanac",{"basePath":772,"githubOwner":773,"githubRepo":774,"locale":18,"slug":769,"type":245},"skills/build-feature-store","pjt222","agent-almanac",{"evaluate":776,"extract":782},{"promptVersionExtension":204,"promptVersionScoring":205,"score":777,"tags":778,"targetMarket":216,"tier":217},99,[779,780,213,781,214],"feature-store","feast","machine-learning",{"commitSha":270},{"parentExtensionId":784,"repoId":785},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[214,780,779,781,213],{"evaluatedAt":788,"extractAt":789,"updatedAt":788},1778696369962,1778695548458,{"_creationTime":791,"_id":792,"community":793,"display":794,"identity":800,"providers":805,"relations":813,"tags":815,"workflow":816},1778685991755.708,"k17eak6qjys6kns9c25d40q9kn86n7g2",{"reviewCount":8},{"description":795,"installMethods":796,"name":798,"sourceUrl":799},"Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. 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HuggingFace ecosystem standard.",{"claudeCode":797},"davila7/claude-code-templates","huggingface-accelerate","https://github.com/davila7/claude-code-templates",{"basePath":801,"githubOwner":802,"githubRepo":803,"locale":18,"slug":804,"type":245},"cli-tool/components/skills/ai-research/distributed-training-accelerate","davila7","claude-code-templates","distributed-training-accelerate",{"evaluate":806,"extract":812},{"promptVersionExtension":204,"promptVersionScoring":205,"score":777,"tags":807,"targetMarket":216,"tier":217},[808,809,810,213,212,811],"distributed-training","pytorch","deep-learning","performance",{"commitSha":270},{"repoId":814},"kd71fzn4s7r0269fkw47wt670n86ndz0",[810,808,212,213,811,809],{"evaluatedAt":817,"extractAt":818,"updatedAt":817},1778686851352,1778685991755,{"_creationTime":820,"_id":821,"community":822,"display":823,"identity":829,"providers":834,"relations":843,"tags":846,"workflow":847},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":824,"installMethods":825,"name":827,"sourceUrl":828},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":826},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":830,"githubOwner":831,"githubRepo":832,"locale":18,"slug":833,"type":245},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":835,"extract":841},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":836,"targetMarket":216,"tier":217},[837,838,839,263,840,211],"finance","trading","market-analysis","typescript",{"commitSha":270,"license":842},"MIT",{"parentExtensionId":844,"repoId":845},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[263,211,837,839,838,840],{"evaluatedAt":848,"extractAt":849,"updatedAt":848},1778701108877,1778696691708,{"_creationTime":851,"_id":852,"community":853,"display":854,"identity":860,"providers":864,"relations":871,"tags":874,"workflow":875},1778699234184.6174,"k174zww66m804nhr89ttra7r6d86nwyg",{"reviewCount":8},{"description":855,"installMethods":856,"name":858,"sourceUrl":859},"Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow",{"claudeCode":857},"Yeachan-Heo/oh-my-claudecode","setup","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":861,"githubOwner":862,"githubRepo":863,"locale":18,"slug":858,"type":245},"skills/setup","Yeachan-Heo","oh-my-claudecode",{"evaluate":865,"extract":870},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":866,"targetMarket":216,"tier":217},[858,867,868,211,869],"routing","configuration","mcp",{"commitSha":270},{"parentExtensionId":872,"repoId":873},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[211,868,869,867,858],{"evaluatedAt":876,"extractAt":877,"updatedAt":876},1778699724286,1778699234184,{"_creationTime":879,"_id":880,"community":881,"display":882,"identity":886,"providers":889,"relations":899,"tags":900,"workflow":901},1778699234184.6157,"k177tdbfgqmwhtaqv771f2ych586nne9",{"reviewCount":8},{"description":883,"installMethods":884,"name":885,"sourceUrl":859},"Worktree-first dev environment manager for issues, PRs, and features with optional tmux sessions",{"claudeCode":857},"Project Session Manager",{"basePath":887,"githubOwner":862,"githubRepo":863,"locale":18,"slug":888,"type":245},"skills/project-session-manager","project-session-manager",{"evaluate":890,"extract":898},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":891,"targetMarket":216,"tier":217},[892,893,894,895,896,211,897],"git","development-environment","workflow","tmux","automation","developer-tool",{"commitSha":270,"license":842},{"parentExtensionId":872,"repoId":873},[896,211,897,893,892,895,894],{"evaluatedAt":902,"extractAt":877,"updatedAt":902},1778699613343]