[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-plugin-huggingface-huggingface-paper-publisher-de":3,"guides-for-huggingface-huggingface-paper-publisher":746,"similar-k178yjakvy2y11set9vw91xvnh86nfxr-de":747},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":14,"identity":255,"isFallback":252,"parentExtension":259,"providers":293,"relations":297,"repo":298,"tags":744,"workflow":745},1778690773482.4832,"k178yjakvy2y11set9vw91xvnh86nfxr",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":12,"sourceUrl":13},"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.",{"claudeCode":12},"huggingface-paper-publisher","https://github.com/huggingface/skills",{"_creationTime":15,"_id":16,"extensionId":5,"locale":17,"result":18,"trustSignals":236,"workflow":253},1778690873816.6904,"kn74j9c68v8gmjabwbsywkr7gx86nvgv","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},[20,25,28,31,35,38,42,46,49,52,56,60,63,67,70,73,76,79,82,85,89,93,97,101,105,108,111,115,119,122,125,128,131,134,137,141,145,149,152,156,159,162,165,168,171,174,177,180,183,186,190,193,196,200],{"category":21,"check":22,"severity":23,"summary":24},"Practical Utility","Problem relevance","pass","The description clearly states the problem of publishing and managing research papers on Hugging Face Hub, including specific capabilities like linking papers to models/datasets.",{"category":21,"check":26,"severity":23,"summary":27},"Unique selling proposition","The skill offers a specialized workflow for managing research papers on Hugging Face Hub, which goes beyond basic API interaction and provides a structured way to integrate research artifacts.",{"category":21,"check":29,"severity":23,"summary":30},"Production readiness","The skill provides a complete lifecycle for paper management, from creation and indexing to linking and authorship verification, supported by clear command-line examples and script dependencies.",{"category":32,"check":33,"severity":23,"summary":34},"Scope","Single responsibility principle","The plugin focuses specifically on the domain of publishing and managing research papers on Hugging Face Hub, with no unrelated capabilities included.",{"category":32,"check":36,"severity":23,"summary":37},"Description quality","The displayed description accurately reflects the skill's capabilities, which include creating paper pages, linking to models/datasets, and generating markdown articles.",{"category":39,"check":40,"severity":23,"summary":41},"Invocation","Scoped tools","The skill exposes specific, well-defined tools like `index`, `link`, `claim`, `create`, and `convert`, avoiding generalist commands.",{"category":43,"check":44,"severity":23,"summary":45},"Documentation","Configuration & parameter reference","All commands and their parameters, including necessary environment variables like HF_TOKEN, are clearly documented with usage examples.",{"category":32,"check":47,"severity":23,"summary":48},"Tool naming","Tool names such as `index`, `link`, `claim`, `create`, and `convert` are descriptive and relevant to the paper publishing domain.",{"category":32,"check":50,"severity":23,"summary":51},"Minimal I/O surface","Input parameters for the scripts are specific (e.g., `--arxiv-id`, `--repo-id`) and the expected outputs are clear based on the commands' functions.",{"category":53,"check":54,"severity":23,"summary":55},"License","License usability","The license is identified as Apache-2.0 via a bundled LICENSE file.",{"category":57,"check":58,"severity":23,"summary":59},"Maintenance","Commit recency","The last commit was on May 12, 2026, which is within the last 3 months.",{"category":57,"check":61,"severity":23,"summary":62},"Dependency Management","Dependencies are managed via PEP 723 inline dependencies, allowing for easy resolution with `uv run`.",{"category":64,"check":65,"severity":23,"summary":66},"Security","Secret Management","The required `HF_TOKEN` is documented as an environment variable and not hardcoded in scripts.",{"category":64,"check":68,"severity":23,"summary":69},"Injection","The scripts are written in Python and use standard libraries, with no indication of loading or executing untrusted third-party data as instructions.",{"category":64,"check":71,"severity":23,"summary":72},"Transitive Supply-Chain Grenades","The skill relies on bundled Python scripts and standard libraries; there is no runtime download of code or data from external URLs.",{"category":64,"check":74,"severity":23,"summary":75},"Sandbox Isolation","The scripts operate within the context of the provided SKILL.md file and operate on relative paths or use standard Hugging Face Hub operations, not affecting files outside the project.",{"category":64,"check":77,"severity":23,"summary":78},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were found in the provided scripts.",{"category":64,"check":80,"severity":23,"summary":81},"Data Exfiltration","The skill's outbound calls are limited to Hugging Face Hub operations, which are documented and expected for its functionality. No confidential data exfiltration is evident.",{"category":64,"check":83,"severity":23,"summary":84},"Hidden Text Tricks","The bundled markdown files and scripts appear free of hidden text tricks or obfuscated instructions.",{"category":86,"check":87,"severity":23,"summary":88},"Hooks","Opaque code execution","The provided scripts are in plain Python and are not obfuscated, minified, or fetched at runtime.",{"category":90,"check":91,"severity":23,"summary":92},"Portability","Structural Assumption","The scripts assume a standard project structure where SKILL.md is present and use relative paths or standard CLI invocation, making them portable.",{"category":94,"check":95,"severity":23,"summary":96},"Trust","Issues Attention","In the last 90 days, 4 issues were opened and 6 were closed, with a closure rate of 60%, indicating good engagement.",{"category":98,"check":99,"severity":23,"summary":100},"Versioning","Release Management","The SKILL.md file declares a version '1.0.0', and installation instructions reference a specific skill name, not a floating branch.",{"category":102,"check":103,"severity":23,"summary":104},"Code Execution","Validation","The Python scripts use standard argument parsing with clear parameter definitions, implying validation of inputs.",{"category":64,"check":106,"severity":23,"summary":107},"Unguarded Destructive Operations","Operations like linking papers and managing visibility modify repository metadata, but are guarded by explicit commands and rely on the user providing a write-enabled token.",{"category":102,"check":109,"severity":23,"summary":110},"Error Handling","The provided usage instructions include specific error handling scenarios like 'Paper Not Found' and 'Permission Denied', suggesting the scripts have error reporting.",{"category":102,"check":112,"severity":113,"summary":114},"Logging","not_applicable","The skill is not explicitly destructive or making outbound calls beyond Hugging Face Hub operations, so local audit logging is not applicable.",{"category":116,"check":117,"severity":113,"summary":118},"Compliance","GDPR","The skill operates on metadata related to papers and Hugging Face Hub artifacts, not personal data, so GDPR is not applicable.",{"category":116,"check":120,"severity":23,"summary":121},"Target market","The extension operates on Hugging Face Hub and arXiv, which are global platforms, and has no regional limitations.",{"category":90,"check":123,"severity":23,"summary":124},"Runtime stability","The skill uses standard Python and the `uv` runner, making it portable across POSIX-compliant systems.",{"category":43,"check":126,"severity":23,"summary":127},"README","The README file exists and clearly outlines the purpose of the Hugging Face Skills repository and how to install and use individual skills.",{"category":32,"check":129,"severity":23,"summary":130},"Tool surface size","The skill exposes a manageable number of distinct commands (index, link, claim, create, convert, check, list-my-papers, search, citation, validate, info, toggle-visibility) for its specialized purpose.",{"category":39,"check":132,"severity":23,"summary":133},"Overlapping near-synonym tools","The tool names are distinct and cover specific functionalities, avoiding near-synonym overlaps.",{"category":43,"check":135,"severity":23,"summary":136},"Phantom features","All features described in the README and SKILL.md, such as paper indexing and linking, have corresponding implemented commands.",{"category":138,"check":139,"severity":23,"summary":140},"Install","Installation instruction","Installation instructions for Claude Code, Codex, Gemini CLI, and Cursor are provided, including copy-paste examples and setup for Hugging Face token authentication.",{"category":142,"check":143,"severity":23,"summary":144},"Errors","Actionable error messages","The skill documentation lists specific error messages and their potential causes and solutions, providing actionable guidance.",{"category":146,"check":147,"severity":23,"summary":148},"Execution","Pinned dependencies","Dependencies are managed via PEP 723 inline dependencies within the script header, ensuring they are pinned and resolved by `uv run`.",{"category":32,"check":150,"severity":113,"summary":151},"Dry-run preview","The primary actions involve metadata updates and generation, which are inherently not destructive and don't require a dry-run mode for inspection.",{"category":153,"check":154,"severity":23,"summary":155},"Protocol","Idempotent retry & timeouts","Operations like linking and indexing are generally idempotent by design (e.g., checking existence before adding). Python scripts typically have default timeouts, and errors are handled.",{"category":116,"check":157,"severity":23,"summary":158},"Telemetry opt-in","There is no mention of telemetry collection in the README or SKILL.md, implying it is not present or is strictly opt-in and undocumented.",{"category":39,"check":160,"severity":23,"summary":161},"Name collisions","The plugin focuses on a single skill, `huggingface-paper-publisher`, avoiding name collisions with other skills or built-in commands.",{"category":39,"check":163,"severity":113,"summary":164},"Hooks-off mechanism","This extension is a plugin providing tools, not one that uses hooks, so a hooks-off mechanism is not applicable.",{"category":39,"check":166,"severity":113,"summary":167},"Hook matcher tightness","There are no hooks declared in this plugin, so hook matcher tightness is not applicable.",{"category":64,"check":169,"severity":113,"summary":170},"Hook security","This plugin does not utilize hooks, so hook security is not applicable.",{"category":86,"check":172,"severity":113,"summary":173},"Silent prompt rewriting","This plugin does not have a `UserPromptSubmit` hook, so silent prompt rewriting is not applicable.",{"category":64,"check":175,"severity":113,"summary":176},"Permission Hook","There are no `PermissionRequest` hooks in this plugin.",{"category":116,"check":178,"severity":113,"summary":179},"Hook privacy","This plugin does not use hooks for logging or telemetry, so hook privacy is not applicable.",{"category":102,"check":181,"severity":113,"summary":182},"Hook dependency","This plugin does not use hooks, so hook dependency checks are not applicable.",{"category":43,"check":184,"severity":113,"summary":185},"Feature Transparency","The plugin does not declare any hooks in `plugin.json` that require explanation in the README.",{"category":187,"check":188,"severity":23,"summary":189},"Convention","Layout convention adherence","The repository structure follows the expected convention with scripts organized appropriately and a clear SKILL.md.",{"category":187,"check":191,"severity":113,"summary":192},"Plugin state","This plugin does not appear to maintain persistent state that would require storage under `${CLAUDE_PLUGIN_DATA}`.",{"category":64,"check":194,"severity":23,"summary":195},"Keychain-stored secrets","The required `HF_TOKEN` is documented as an environment variable, not stored in `settings.json`, and thus would not be keychain-stored by default.",{"category":197,"check":198,"severity":23,"summary":199},"Dependencies","Tagged release sourcing","The plugin's dependencies are declared via PEP 723 inline dependencies, which are resolved by `uv run` and effectively pinned.",{"category":201,"check":202,"severity":23,"summary":203},"Installation","Clean uninstall","The installation process involves adding a plugin marketplace and installing a skill, which are standard operations managed by the agent and do not involve background daemons or persistent system modifications.",1778690873705,"This plugin provides a suite of Python scripts to manage research papers on the Hugging Face Hub. It allows users to index papers from arXiv, link them to models and datasets with proper citations, claim authorship, and generate markdown-based research articles using various templates.",[207,208,209,210,211],"Index papers from arXiv","Link papers to models/datasets","Claim and verify authorship","Generate markdown research articles","Manage paper visibility on profile",[213,214,215],"Directly submitting papers to arXiv","Managing general Hugging Face Hub repositories (models, datasets, spaces) unrelated to papers","Providing a full-fledged LaTeX editor for paper writing","3.0.0","4.4.0","To streamline the process of publishing, managing, and integrating research papers within the Hugging Face ecosystem for AI researchers and engineers.","The plugin demonstrates excellent adherence to all checks, with no critical or warning findings. The few 'not_applicable' findings are appropriate for the plugin's scope. The high commit recency and active issue resolution contribute to the top score.",98,"A high-quality plugin for managing research papers on Hugging Face Hub.",[223,224,225,226,227,228],"huggingface","research","papers","publishing","documentation","arxiv","global","verified",[232,233,234,235],"Publishing a new research paper and linking it to a model on Hugging Face","Updating existing model or dataset cards with new paper references","Managing personal research paper portfolio on Hugging Face","Generating a professional markdown research article from a template",{"codeQuality":237,"collectedAt":239,"documentation":240,"maintenance":243,"security":249,"testCoverage":251},{"hasLockfile":238},false,1778690852819,{"descriptionLength":241,"readmeSize":242},204,9821,{"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},1778690873816,{"basePath":256,"githubOwner":223,"githubRepo":257,"locale":17,"slug":12,"type":258},"skills/huggingface-paper-publisher","skills","plugin",{"_creationTime":260,"_id":261,"community":262,"display":263,"identity":268,"parentExtension":271,"providers":272,"relations":287,"tags":289,"workflow":290},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":223,"githubRepo":257,"locale":17,"slug":257,"type":270},"","marketplace",null,{"evaluate":273,"extract":281},{"promptVersionExtension":274,"promptVersionScoring":217,"score":275,"tags":276,"targetMarket":229,"tier":230},"3.1.0",95,[277,223,278,279,224,280],"ai-ml","datasets","models","developer-tools",{"commitSha":282,"marketplace":283,"plugin":285},"HEAD",{"name":267,"pluginCount":284},14,{"mcpCount":8,"provider":286,"skillCount":8},"classify",{"repoId":288},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[277,278,280,223,279,224],{"evaluatedAt":291,"extractAt":292,"updatedAt":291},1778690814090,1778690773482,{"evaluate":294,"extract":296},{"promptVersionExtension":216,"promptVersionScoring":217,"score":220,"tags":295,"targetMarket":229,"tier":230},[223,224,225,226,227,228],{"commitSha":282},{"parentExtensionId":261,"repoId":288},{"_creationTime":299,"_id":288,"identity":300,"providers":301,"workflow":740},1778689536128.5474,{"githubOwner":223,"githubRepo":257,"sourceUrl":13},{"classify":302,"discover":733,"github":736},{"commitSha":282,"extensions":303},[304,317,326,334,339,347,355,363,371,379,387,395,403,411,419,427,470,479,485,491,508,514,521,563,574,593,599,619,631,655,713],{"basePath":269,"description":264,"displayName":267,"installMethods":305,"rationale":306,"selectedPaths":307,"source":316,"sourceLanguage":17,"type":270},{"claudeCode":266},"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":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":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":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":329},"inline plugin source from marketplace.json at skills/huggingface-local-models",[333],{"path":325,"priority":315},{"basePath":256,"description":10,"displayName":12,"installMethods":335,"rationale":336,"selectedPaths":337,"source":316,"sourceLanguage":17,"type":258},{"claudeCode":12},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[338],{"path":325,"priority":315},{"basePath":340,"description":341,"displayName":342,"installMethods":343,"rationale":344,"selectedPaths":345,"source":316,"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":342},"inline plugin source from marketplace.json at skills/huggingface-papers",[346],{"path":325,"priority":315},{"basePath":348,"description":349,"displayName":350,"installMethods":351,"rationale":352,"selectedPaths":353,"source":316,"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":350},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[354],{"path":325,"priority":315},{"basePath":356,"description":357,"displayName":358,"installMethods":359,"rationale":360,"selectedPaths":361,"source":316,"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":358},"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":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":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":258},"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":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":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":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":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":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":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":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":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":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":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":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":422},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[426],{"path":325,"priority":315},{"basePath":269,"description":264,"displayName":267,"installMethods":428,"license":250,"rationale":429,"selectedPaths":430,"source":316,"sourceLanguage":17,"type":258},{"claudeCode":267},"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":266},"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":266},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[484],{"path":325,"priority":310},{"basePath":356,"description":486,"displayName":358,"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":266},"SKILL.md frontmatter at skills/huggingface-best/SKILL.md",[490],{"path":325,"priority":310},{"basePath":348,"description":492,"displayName":350,"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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