[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-plugin-huggingface-skills-en":3,"guides-for-huggingface-skills":1002,"similar-k175g1spb5757qt4tnj9cktcn986mshy-en":1003},{"_creationTime":4,"_id":5,"children":6,"community":344,"display":345,"evaluation":350,"identity":581,"isFallback":564,"parentExtension":584,"providers":585,"relations":592,"repo":593,"tags":1000,"workflow":1001},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",[7,44,70,94,117,140,163,186,209,228,250,273,298,320],{"_creationTime":8,"_id":9,"community":10,"display":12,"identity":18,"providers":24,"relations":38,"tags":40,"workflow":41},1778690773482.4866,"k17a3mmgvm5hj49twj487hp64186n2qa",{"reviewCount":11},0,{"description":13,"installMethods":14,"name":16,"sourceUrl":17},"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":15},"huggingface/skills","hf-cli","https://github.com/huggingface/skills",{"basePath":19,"githubOwner":20,"githubRepo":21,"locale":22,"slug":16,"type":23},"skills/hf-cli","huggingface","skills","en","skill",{"evaluate":25,"extract":36},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":29,"targetMarket":34,"tier":35},"3.0.0","4.4.0",100,[30,20,31,32,33],"cli","mlops","data-management","model-management","global","verified",{"commitSha":37},"HEAD",{"parentExtensionId":5,"repoId":39},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[30,32,20,31,33],{"evaluatedAt":42,"extractAt":43,"updatedAt":42},1778691223210,1778690773482,{"_creationTime":45,"_id":46,"community":47,"display":48,"identity":52,"providers":55,"relations":66,"tags":67,"workflow":68},1778690773482.4868,"k1762e6s5rwd7spcymdvpb9rtn86m0jm",{"reviewCount":11},{"description":49,"installMethods":50,"name":51,"sourceUrl":17},"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":15},"HuggingFace Best Model Finder",{"basePath":53,"githubOwner":20,"githubRepo":21,"locale":22,"slug":54,"type":23},"skills/huggingface-best","huggingface-best",{"evaluate":56,"extract":64},{"promptVersionExtension":26,"promptVersionScoring":27,"score":57,"tags":58,"targetMarket":34,"tier":35},99,[20,59,60,61,62,63],"llm","model-recommendation","benchmarks","leaderboards","ai-models",{"commitSha":37,"license":65},"Apache-2.0",{"parentExtensionId":5,"repoId":39},[63,61,20,62,59,60],{"evaluatedAt":69,"extractAt":43,"updatedAt":69},1778691241235,{"_creationTime":71,"_id":72,"community":73,"display":74,"identity":78,"providers":80,"relations":90,"tags":91,"workflow":92},1778690773482.487,"k1726vs7xfdjf1be08gkbz6v1s86mxk2",{"reviewCount":11},{"description":75,"installMethods":76,"name":77,"sourceUrl":17},"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":15},"huggingface-community-evals",{"basePath":79,"githubOwner":20,"githubRepo":21,"locale":22,"slug":77,"type":23},"skills/huggingface-community-evals",{"evaluate":81,"extract":89},{"promptVersionExtension":26,"promptVersionScoring":27,"score":82,"tags":83,"targetMarket":34,"tier":35},98,[20,84,59,85,86,87,88],"evaluation","inspect-ai","lighteval","gpu","vllm",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[84,87,20,85,86,59,88],{"evaluatedAt":93,"extractAt":43,"updatedAt":93},1778691261342,{"_creationTime":95,"_id":96,"community":97,"display":98,"identity":102,"providers":104,"relations":113,"tags":114,"workflow":115},1778690773482.4873,"k1718qk3qkn3b221p8505fk9w986nr2t",{"reviewCount":11},{"description":99,"installMethods":100,"name":101,"sourceUrl":17},"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":15},"huggingface-datasets",{"basePath":103,"githubOwner":20,"githubRepo":21,"locale":22,"slug":101,"type":23},"skills/huggingface-datasets",{"evaluate":105,"extract":112},{"promptVersionExtension":26,"promptVersionScoring":27,"score":106,"tags":107,"targetMarket":34,"tier":35},97,[20,108,109,110,111],"datasets","api","data-exploration","data-extraction",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[109,110,111,108,20],{"evaluatedAt":116,"extractAt":43,"updatedAt":116},1778691285065,{"_creationTime":118,"_id":119,"community":120,"display":121,"identity":125,"providers":127,"relations":136,"tags":137,"workflow":138},1778690773482.4875,"k1738z37awwqt5ngyf19jvq0dn86n4zs",{"reviewCount":11},{"description":122,"installMethods":123,"name":124,"sourceUrl":17},"Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.",{"claudeCode":15},"huggingface-gradio",{"basePath":126,"githubOwner":20,"githubRepo":21,"locale":22,"slug":124,"type":23},"skills/huggingface-gradio",{"evaluate":128,"extract":135},{"promptVersionExtension":26,"promptVersionScoring":27,"score":82,"tags":129,"targetMarket":34,"tier":35},[130,131,132,133,134],"python","web-ui","gradio","demo-building","machine-learning",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[133,132,134,130,131],{"evaluatedAt":139,"extractAt":43,"updatedAt":139},1778691303545,{"_creationTime":141,"_id":142,"community":143,"display":144,"identity":148,"providers":150,"relations":159,"tags":160,"workflow":161},1778690773482.4878,"k17aqa68b1vx1r0j9feqm2kggh86nv3v",{"reviewCount":11},{"description":145,"installMethods":146,"name":147,"sourceUrl":17},"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":15},"huggingface-llm-trainer",{"basePath":149,"githubOwner":20,"githubRepo":21,"locale":22,"slug":147,"type":23},"skills/huggingface-llm-trainer",{"evaluate":151,"extract":158},{"promptVersionExtension":26,"promptVersionScoring":27,"score":57,"tags":152,"targetMarket":34,"tier":35},[59,153,154,155,156,157,130,134],"fine-tuning","trl","unsloth","huggingface-jobs","gguf",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[153,157,156,59,134,130,154,155],{"evaluatedAt":162,"extractAt":43,"updatedAt":162},1778691314030,{"_creationTime":164,"_id":165,"community":166,"display":167,"identity":171,"providers":174,"relations":182,"tags":183,"workflow":184},1778690773482.488,"k170rhxp71d7qg10dfv2hybw1n86n8dm",{"reviewCount":11},{"description":168,"installMethods":169,"name":170,"sourceUrl":17},"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.",{"claudeCode":15},"Hugging Face Local Models",{"basePath":172,"githubOwner":20,"githubRepo":21,"locale":22,"slug":173,"type":23},"skills/huggingface-local-models","huggingface-local-models",{"evaluate":175,"extract":181},{"promptVersionExtension":26,"promptVersionScoring":27,"score":176,"tags":177,"targetMarket":34,"tier":35},95,[59,178,179,180,157,20],"local","models","llama-cpp",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[157,20,180,59,178,179],{"evaluatedAt":185,"extractAt":43,"updatedAt":185},1778691335553,{"_creationTime":187,"_id":188,"community":189,"display":190,"identity":194,"providers":196,"relations":205,"tags":206,"workflow":207},1778690773482.4883,"k17fbzfkb9jcq3hk2k6f27d6an86ne59",{"reviewCount":11},{"description":191,"installMethods":192,"name":193,"sourceUrl":17},"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":15},"huggingface-paper-publisher",{"basePath":195,"githubOwner":20,"githubRepo":21,"locale":22,"slug":193,"type":23},"skills/huggingface-paper-publisher",{"evaluate":197,"extract":204},{"promptVersionExtension":26,"promptVersionScoring":27,"score":106,"tags":198,"targetMarket":34,"tier":35},[20,199,200,201,202,203],"research","publishing","arxiv","documentation","workflow",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[201,202,20,200,199,203],{"evaluatedAt":208,"extractAt":43,"updatedAt":208},1778691354790,{"_creationTime":210,"_id":211,"community":212,"display":213,"identity":217,"providers":219,"relations":224,"tags":225,"workflow":226},1778690773482.4885,"k177asm0v1bhrzc0gq52a936z586memq",{"reviewCount":11},{"description":214,"installMethods":215,"name":216,"sourceUrl":17},"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":15},"huggingface-papers",{"basePath":218,"githubOwner":20,"githubRepo":21,"locale":22,"slug":216,"type":23},"skills/huggingface-papers",{"evaluate":220,"extract":223},{"promptVersionExtension":26,"promptVersionScoring":27,"score":57,"tags":221,"targetMarket":34,"tier":35},[20,222,199,109,201],"papers",{"commitSha":37},{"parentExtensionId":5,"repoId":39},[109,201,20,222,199],{"evaluatedAt":227,"extractAt":43,"updatedAt":227},1778691369571,{"_creationTime":229,"_id":230,"community":231,"display":232,"identity":236,"providers":239,"relations":246,"tags":247,"workflow":248},1778690773482.4888,"k1726ndq21b032g066r345zd6s86m3d9",{"reviewCount":11},{"description":233,"installMethods":234,"name":235,"sourceUrl":17},"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":15},"Hugging Face Tool Builder",{"basePath":237,"githubOwner":20,"githubRepo":21,"locale":22,"slug":238,"type":23},"skills/huggingface-tool-builder","huggingface-tool-builder",{"evaluate":240,"extract":245},{"promptVersionExtension":26,"promptVersionScoring":27,"score":82,"tags":241,"targetMarket":34,"tier":35},[20,109,242,243,244],"scripting","automation","data-processing",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[109,243,244,20,242],{"evaluatedAt":249,"extractAt":43,"updatedAt":249},1778691398007,{"_creationTime":251,"_id":252,"community":253,"display":254,"identity":258,"providers":261,"relations":269,"tags":270,"workflow":271},1778690773482.489,"k17826t6czszbm021pkj9f9hes86nqv1",{"reviewCount":11},{"description":255,"installMethods":256,"name":257,"sourceUrl":17},"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":15},"Trackio",{"basePath":259,"githubOwner":20,"githubRepo":21,"locale":22,"slug":260,"type":23},"skills/huggingface-trackio","huggingface-trackio",{"evaluate":262,"extract":268},{"promptVersionExtension":26,"promptVersionScoring":27,"score":263,"tags":264,"targetMarket":34,"tier":267},85,[265,266,130,30,20],"ml","experiment-tracking","community",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[30,266,20,265,130],{"evaluatedAt":272,"extractAt":43,"updatedAt":272},1778691424326,{"_creationTime":274,"_id":275,"community":276,"display":277,"identity":281,"providers":284,"relations":294,"tags":295,"workflow":296},1778690773482.4893,"k171rcpcqjgapdaj3pg89j9tp186mf2w",{"reviewCount":11},{"description":278,"installMethods":279,"name":280,"sourceUrl":17},"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":15},"Hugging Face Vision Trainer",{"basePath":282,"githubOwner":20,"githubRepo":21,"locale":22,"slug":283,"type":23},"skills/huggingface-vision-trainer","huggingface-vision-trainer",{"evaluate":285,"extract":293},{"promptVersionExtension":26,"promptVersionScoring":27,"score":57,"tags":286,"targetMarket":34,"tier":35},[287,288,289,290,291,20,292],"computer-vision","object-detection","image-classification","segmentation","deep-learning","cloud-training",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[292,287,291,20,289,288,290],{"evaluatedAt":297,"extractAt":43,"updatedAt":297},1778691436038,{"_creationTime":299,"_id":300,"community":301,"display":302,"identity":306,"providers":309,"relations":316,"tags":317,"workflow":318},1778690773482.4895,"k17eq44byzzy319j4x3k26y4vx86n4a3",{"reviewCount":11},{"description":303,"installMethods":304,"name":305,"sourceUrl":17},"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":15},"Train Sentence-Transformers",{"basePath":307,"githubOwner":20,"githubRepo":21,"locale":22,"slug":308,"type":23},"skills/train-sentence-transformers","train-sentence-transformers",{"evaluate":310,"extract":315},{"promptVersionExtension":26,"promptVersionScoring":27,"score":82,"tags":311,"targetMarket":34,"tier":35},[312,313,134,291,153,314],"sentence-transformers","nlp","embeddings",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[291,314,153,134,313,312],{"evaluatedAt":319,"extractAt":43,"updatedAt":319},1778691446975,{"_creationTime":321,"_id":322,"community":323,"display":324,"identity":328,"providers":331,"relations":340,"tags":341,"workflow":342},1778690773482.4897,"k1758gawxcg89ba8b2srtfawhd86nmcs",{"reviewCount":11},{"description":325,"installMethods":326,"name":327,"sourceUrl":17},"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":15},"Transformers.js",{"basePath":329,"githubOwner":20,"githubRepo":21,"locale":22,"slug":330,"type":23},"skills/transformers-js","transformers-js",{"evaluate":332,"extract":339},{"promptVersionExtension":26,"promptVersionScoring":27,"score":57,"tags":333,"targetMarket":34,"tier":35},[134,334,335,313,287,336,337,338],"javascript","typescript","audio","webgpu","wasm",{"commitSha":37,"license":65},{"parentExtensionId":5,"repoId":39},[336,287,334,134,313,335,338,337],{"evaluatedAt":343,"extractAt":43,"updatedAt":343},1778691465826,{"reviewCount":11},{"description":346,"installMethods":347,"name":349,"sourceUrl":17},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":348},"huggingface-skills","Hugging Face Skills",{"_creationTime":351,"_id":352,"extensionId":5,"locale":22,"result":353,"trustSignals":562,"workflow":579},1778691185872.6636,"kn73a04qdezrjhptqxkazhfxdd86mhba",{"checks":354,"evaluatedAt":537,"extensionSummary":538,"features":539,"nonGoals":545,"promptVersionExtension":26,"promptVersionScoring":27,"purpose":550,"rationale":551,"score":82,"summary":552,"tags":553,"targetMarket":34,"tier":35,"useCases":556},[355,360,363,366,370,373,377,381,384,387,391,395,398,402,405,408,411,414,417,420,424,428,432,436,440,443,446,449,453,456,459,462,465,468,471,475,479,483,487,491,494,497,500,503,505,508,511,514,516,519,523,526,529,533],{"category":356,"check":357,"severity":358,"summary":359},"Practical Utility","Problem relevance","pass","The description clearly states the problem of performing AI/ML tasks on Hugging Face Hub, including dataset creation, model training, evaluation, and paper publishing.",{"category":356,"check":361,"severity":358,"summary":362},"Unique selling proposition","The collection of skills provides specialized tools for various AI/ML tasks on Hugging Face, going beyond generic API wrappers by offering tailored workflows and guidance for each task.",{"category":356,"check":364,"severity":358,"summary":365},"Production readiness","The skills cover a comprehensive lifecycle for AI/ML tasks on Hugging Face, from data handling and training to evaluation and publication, with detailed scripts and guidance suggesting production-ready capabilities.",{"category":367,"check":368,"severity":358,"summary":369},"Scope","Single responsibility principle","The plugin bundles a coherent set of skills related to Hugging Face AI/ML workflows, covering distinct but related tasks within that domain.",{"category":367,"check":371,"severity":358,"summary":372},"Description quality","The displayed description accurately and concisely summarizes the plugin's purpose and capabilities.",{"category":374,"check":375,"severity":358,"summary":376},"Invocation","Scoped tools","The plugin's individual skills appear to expose scoped tools (e.g., `hf download`, `hf auth login`, `uv run scripts/train_sft_example.py`), promoting clear intent and reducing attack surface.",{"category":378,"check":379,"severity":358,"summary":380},"Documentation","Configuration & parameter reference","SKILL.md files for individual skills provide detailed documentation on parameters, usage, and prerequisites, including specific script arguments and environment variables.",{"category":367,"check":382,"severity":358,"summary":383},"Tool naming","Tools and scripts within individual skills (e.g., `hf-cli` commands, `uv run scripts/...`) are descriptively named and follow kebab-case conventions.",{"category":367,"check":385,"severity":358,"summary":386},"Minimal I/O surface","Individual skill scripts and CLI commands appear to focus on necessary inputs and outputs, with examples showing specific arguments rather than free-form context blobs.",{"category":388,"check":389,"severity":358,"summary":390},"License","License usability","The extension is licensed under Apache-2.0, a permissive open-source license, clearly declared in multiple manifests.",{"category":392,"check":393,"severity":358,"summary":394},"Maintenance","Commit recency","The repository shows recent activity with a commit on May 12, 2026, indicating active maintenance.",{"category":392,"check":396,"severity":358,"summary":397},"Dependency Management","The use of UV scripts with PEP 723 inline dependencies suggests a robust and manageable approach to third-party dependencies.",{"category":399,"check":400,"severity":358,"summary":401},"Security","Secret Management","The documentation consistently emphasizes using HF_TOKEN via environment variables or job secrets, with clear instructions on how to manage it securely, and avoids hardcoding secrets in scripts.",{"category":399,"check":403,"severity":358,"summary":404},"Injection","The skills primarily rely on structured CLI arguments, API calls, and documented script inputs, minimizing the risk of untrusted data injection.",{"category":399,"check":406,"severity":358,"summary":407},"Transitive Supply-Chain Grenades","Scripts are provided within the repository and use PEP 723 for dependencies, indicating that external code is not fetched and executed at runtime.",{"category":399,"check":409,"severity":358,"summary":410},"Sandbox Isolation","The focus on CLI tools and Python scripts executed within defined jobs or local environments, along with relative paths, suggests adherence to sandbox isolation principles.",{"category":399,"check":412,"severity":358,"summary":413},"Sandbox escape primitives","No evidence of detached processes or retry loops around denied tool calls was observed in the provided documentation and script structures.",{"category":399,"check":415,"severity":358,"summary":416},"Data Exfiltration","The skills primarily interact with Hugging Face APIs and local files, with no undocumented outbound calls or submission of confidential data to unknown third parties.",{"category":399,"check":418,"severity":358,"summary":419},"Hidden Text Tricks","The documentation and script examples do not contain hidden text tricks or obfuscation intended to mislead the model or curator.",{"category":421,"check":422,"severity":358,"summary":423},"Hooks","Opaque code execution","The extension relies on standard Python scripts and CLI tools, without evidence of obfuscated code, base64 payloads, or runtime code fetching.",{"category":425,"check":426,"severity":358,"summary":427},"Portability","Structural Assumption","Scripts and CLI commands appear to be designed for portability, using standard arguments and relying on the `hf` CLI or Python packages rather than specific project structures.",{"category":429,"check":430,"severity":358,"summary":431},"Trust","Issues Attention","With 4 open and 6 closed issues in the last 90 days, the closure rate is likely sufficient, indicating active maintainer engagement.",{"category":433,"check":434,"severity":358,"summary":435},"Versioning","Release Management","The extension has a manifest version (1.0.2) and a recent commit date, indicating a managed release process.",{"category":437,"check":438,"severity":358,"summary":439},"Code Execution","Validation","The documentation for individual skills (e.g., `huggingface-vision-trainer`, `hf-cli`) emphasizes using parsed arguments and validated inputs for scripts and CLI commands.",{"category":399,"check":441,"severity":358,"summary":442},"Unguarded Destructive Operations","The provided scripts and CLI commands focus on model interaction, data handling, and training, with no evidence of unguarded destructive operations.",{"category":437,"check":444,"severity":358,"summary":445},"Error Handling","The documentation and example scripts within various skills demonstrate robust error handling, including explicit messages, fallbacks, and guidance on troubleshooting common issues.",{"category":437,"check":447,"severity":358,"summary":448},"Logging","The `trackio` skill and examples within training scripts indicate support for logging metrics and events, with references to local log files and HF Space synchronization.",{"category":450,"check":451,"severity":358,"summary":452},"Compliance","GDPR","The skills focus on metadata, model artifacts, and training processes, with no indication of operating on personal data without appropriate sanitization or consent.",{"category":450,"check":454,"severity":358,"summary":455},"Target market","The extension operates within the global AI/ML community and Hugging Face ecosystem, with no specific regional or jurisdictional limitations mentioned.",{"category":425,"check":457,"severity":358,"summary":458},"Runtime stability","The use of Python scripts with PEP 723 dependencies, `uv run`, and the `hf` CLI suggests good cross-platform compatibility, with clear guidance for different environments.",{"category":378,"check":460,"severity":358,"summary":461},"README","The main README.md file is comprehensive, clearly states the extension's purpose, and provides detailed installation and usage instructions.",{"category":367,"check":463,"severity":358,"summary":464},"Tool surface size","The plugin exposes a curated set of distinct skills rather than an overwhelming number of individual tools, maintaining a manageable scope.",{"category":374,"check":466,"severity":358,"summary":467},"Overlapping near-synonym tools","The individual skills appear to have distinct functionalities (e.g., `hf-cli`, `huggingface-datasets`, `huggingface-llm-trainer`), avoiding significant overlap in tool names or purposes.",{"category":378,"check":469,"severity":358,"summary":470},"Phantom features","Features described in the READMEs and SKILL.md files correspond to actual tools, scripts, and capabilities implemented within the plugin's skills.",{"category":472,"check":473,"severity":358,"summary":474},"Install","Installation instruction","Installation instructions are clearly provided for multiple environments (Claude Code, Codex, Gemini CLI, Cursor) and include copy-pasteable commands and examples.",{"category":476,"check":477,"severity":358,"summary":478},"Errors","Actionable error messages","Individual skills' documentation and troubleshooting sections provide actionable error messages, root causes, and remediation steps.",{"category":480,"check":481,"severity":358,"summary":482},"Execution","Pinned dependencies","The use of PEP 723 inline dependencies within scripts implies pinned versions, ensuring reproducible execution 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