[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-transformers-js-zh-CN":3,"guides-for-huggingface-transformers-js":753,"similar-k1758gawxcg89ba8b2srtfawhd86nmcs-zh-CN":754},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":259,"isFallback":256,"parentExtension":265,"providers":300,"relations":304,"repo":305,"tags":751,"workflow":752},1778690773482.4897,"k1758gawxcg89ba8b2srtfawhd86nmcs",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","Transformers.js","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":239,"workflow":257},1778691465826.8005,"kn70xch43h3c3b4nzy7v9pz4js86n11e","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"practices":204,"prerequisites":208,"promptVersionExtension":212,"promptVersionScoring":213,"purpose":214,"rationale":215,"score":216,"summary":217,"tags":218,"targetMarket":227,"tier":228,"useCases":229,"workflow":234},[21,26,29,32,36,39,44,48,51,54,58,62,65,69,72,75,78,81,84,87,90,94,98,102,106,109,113,116,120,123,127,130,133,136,139,142,145,149,153,155,156,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem: running state-of-the-art machine learning models directly in JavaScript/TypeScript, addressing the need for client-side or server-side ML without Python dependencies.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers significant value by enabling advanced ML models to run directly in JavaScript/TypeScript environments, which is a substantial enhancement over standard browser/Node.js capabilities.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill appears production-ready, supporting various runtimes (browser, Node.js, Bun, Deno) and execution backends (WebGPU/WASM), with clear installation, configuration, and usage examples covering the full lifecycle.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on providing machine learning model inference capabilities via Transformers.js, operating within a coherent domain without extending into unrelated areas.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the skill's capabilities, covering supported tasks, runtimes, and backend technologies.",{"category":40,"check":41,"severity":42,"summary":43},"Invocation","Scoped tools","not_applicable","This is a skill that wraps a library, not a set of distinct tools. The evaluation of scoped tools is not applicable here.",{"category":45,"check":46,"severity":24,"summary":47},"Documentation","Configuration & parameter reference","The SKILL.md provides detailed documentation for pipeline options, environment configuration, and model selection, including defaults and usage patterns.",{"category":33,"check":49,"severity":42,"summary":50},"Tool naming","As this skill wraps a library and does not expose distinct named tools, this check is not applicable.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The skill's interfaces (pipeline function, generation parameters) are well-defined and accept specific inputs relevant to the task, returning focused outputs.",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The extension is licensed under the Apache-2.0 license, which is a permissive open-source license compatible with most use cases.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The latest commit was on 2026-05-12, indicating recent maintenance activity.",{"category":59,"check":63,"severity":42,"summary":64},"Dependency Management","The skill itself does not appear to manage external dependencies beyond the core Transformers.js library, which is bundled. Therefore, this check is not applicable.",{"category":66,"check":67,"severity":42,"summary":68},"Security","Secret Management","The skill does not appear to handle or require secrets, making this check not applicable.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill's documentation and examples emphasize treating model inputs as data and do not suggest executing arbitrary instructions from loaded content.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill focuses on running pre-trained models and does not involve runtime downloads of executable code or instructions from untrusted remote sources.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill operates within the JavaScript/TypeScript runtime (browser or Node.js) and does not appear to perform operations that would violate sandbox isolation.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were observed in the provided documentation and examples.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill's primary function is model inference; it does not inherently include mechanisms for exfiltrating confidential data. User-provided inputs are processed locally.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled markdown files and documentation do not appear to contain hidden steering tricks, invisible Unicode characters, or other obfuscation techniques.",{"category":66,"check":88,"severity":24,"summary":89},"Opaque code execution","The provided documentation and code examples use standard, readable JavaScript/TypeScript; no obfuscated code or runtime script fetching was observed.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill does not make structural assumptions about the user's project organization outside of its own bundle, focusing on library usage.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","There were 4 issues opened and 6 closed in the last 90 days, indicating active maintenance and a reasonable closure rate.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The skill metadata declares version '4.x', and the repository likely follows semver practices, indicating a managed release process.",{"category":103,"check":104,"severity":42,"summary":105},"Execution","Pinned dependencies","The skill itself is a JavaScript library and doesn't bundle scripts with external dependencies that require pinning via lockfiles. The core Transformers.js library would handle its own dependencies.",{"category":33,"check":107,"severity":42,"summary":108},"Dry-run preview","The skill performs local model inference and does not involve state-changing operations or outbound data submission, making a dry-run mode not applicable.",{"category":110,"check":111,"severity":42,"summary":112},"Protocol","Idempotent retry & timeouts","As the skill performs local model inference and does not make external calls or state-changing operations, idempotency and timeouts in this context are not applicable.",{"category":66,"check":114,"severity":24,"summary":115},"Unguarded Destructive Operations","The skill's core function is model inference, which is not a destructive operation and does not require a confirmation gate.",{"category":117,"check":118,"severity":24,"summary":119},"Code Execution","Error Handling","The documentation provides examples of error handling using try-catch blocks and suggests checking for specific error types, indicating a robust approach.",{"category":117,"check":121,"severity":42,"summary":122},"Logging","The skill performs local inference and does not have destructive actions or outbound calls that would necessitate local audit logging.",{"category":124,"check":125,"severity":24,"summary":126},"Compliance","GDPR","The skill processes user-provided text locally for inference and does not inherently handle personal data or submit it to third parties.",{"category":124,"check":128,"severity":24,"summary":129},"Target market","The skill is designed for broad use across browsers and server-side JavaScript runtimes globally, with no discernible regional or jurisdictional restrictions.",{"category":91,"check":131,"severity":24,"summary":132},"Runtime stability","The skill explicitly supports multiple runtimes (browser, Node.js, Bun, Deno) and execution backends (WebGPU/WASM), indicating good cross-platform stability.",{"category":45,"check":134,"severity":24,"summary":135},"README","The README file provides a clear overview of the skill's purpose, installation, and usage across different platforms.",{"category":33,"check":137,"severity":42,"summary":138},"Tool surface size","This skill acts as a wrapper for a library and does not expose multiple distinct tools or commands, making this check not applicable.",{"category":40,"check":140,"severity":42,"summary":141},"Overlapping near-synonym tools","As this skill does not expose multiple distinct tools, there are no overlapping near-synonym tools to evaluate.",{"category":45,"check":143,"severity":24,"summary":144},"Phantom features","All advertised features, such as NLP, computer vision, and audio tasks, have corresponding documentation and examples demonstrating their implementation.",{"category":146,"check":147,"severity":24,"summary":148},"Install","Installation instruction","Clear installation instructions for NPM (browser/Node.js) and CDN usage are provided in the SKILL.md and README.",{"category":150,"check":151,"severity":24,"summary":152},"Errors","Actionable error messages","The documentation includes an 'Error Handling' section with examples and suggests checking for specific error types, providing actionable guidance.",{"category":103,"check":104,"severity":42,"summary":154},"The skill itself doesn't bundle scripts with specific external dependencies requiring pinning; it relies on the Transformers.js library.",{"category":33,"check":107,"severity":42,"summary":108},{"category":110,"check":111,"severity":42,"summary":112},{"category":124,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The documentation does not mention any telemetry collection; assuming no telemetry is emitted by default, this check passes.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The skill clearly defines its purpose as running ML models in JS/TS and specifies when to use it (client-side ML, no Python backend) and implicitly when not to (complex model training).",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core capability and supported tasks.",{"category":45,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md content is well-structured with clear sections and references to external documentation, keeping the main file concise.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Detailed information like configuration, examples, and advanced topics are correctly delegated to separate reference files, ensuring progressive disclosure.",{"category":170,"check":174,"severity":42,"summary":175},"Forked exploration","This skill is for inference and does not involve deep code review or exploration that would necessitate a `context: fork` setting.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","Multiple ready-to-use examples for browser, Node.js, React, and Express API are provided, covering core functionalities and demonstrating observable outcomes.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The skill documentation addresses potential issues like memory management, WebGPU errors, and model not found errors, providing recovery steps.",{"category":117,"check":183,"severity":42,"summary":184},"Tool Fallback","The skill does not rely on external tools like a custom MCP server, making this check not applicable.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The documentation implicitly encourages handling errors and disposing of pipelines, suggesting a controlled workflow that would halt on unexpected states.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill focuses on its direct functionality and does not appear to implicitly rely on other skills being loaded in the same session.",1778691465525,"This skill leverages Transformers.js to enable the execution of state-of-the-art machine learning models within JavaScript/TypeScript environments, including browsers and server-side runtimes like Node.js, Bun, and Deno. It supports a wide range of tasks across NLP, computer vision, and audio processing using WebGPU and WASM backends with models from Hugging Face Hub.",[195,196,197,198,199],"Run NLP models (classification, translation, summarization)","Perform computer vision tasks (image classification, object detection)","Process audio (speech recognition, classification)","Support multimodal AI applications","Execute in browsers and server-side runtimes (Node.js, Bun, Deno)",[201,202,203],"Training large-scale machine learning models","Replacing dedicated Python ML backends for complex training pipelines","Executing models that require specialized hardware not supported by WebGPU/WASM",[205,206,207],"Model inference","JavaScript/TypeScript development","Web machine learning",[209,210,211],"Node.js 18+ (or compatible Bun/Deno runtime) or modern browser with ES modules support","WebGPU requires runtime and hardware support; WASM is the broad fallback","Internet access for downloading models from Hugging Face Hub (optional if using local models)","3.0.0","4.4.0","To empower developers to integrate powerful machine learning capabilities directly into their JavaScript/TypeScript applications without the need for Python-based backend infrastructure.","Excellent documentation and examples, clear purpose, and robust error handling support. No critical or warning findings.",99,"A high-quality skill for running advanced ML models directly in JavaScript/TypeScript environments.",[219,220,221,222,223,224,225,226],"machine-learning","javascript","typescript","nlp","computer-vision","audio","webgpu","wasm","global","verified",[230,231,232,233],"Building client-side ML features in web applications","Developing serverless ML inference with Node.js","Creating offline-capable AI applications","Integrating ML models into existing JavaScript projects",[235,236,237,238],"Initialize pipeline with task, model, and optional configurations (device, dtype, etc.)","Process input data through the pipeline","Receive model output (e.g., generated text, classification labels, embeddings)","Dispose of the pipeline to free resources",{"codeQuality":240,"collectedAt":242,"documentation":243,"maintenance":246,"security":253,"testCoverage":255},{"hasLockfile":241},false,1778691447444,{"descriptionLength":244,"readmeSize":245},425,9821,{"closedIssues90d":247,"forks":248,"hasChangelog":241,"manifestVersion":249,"openIssues90d":250,"pushedAt":251,"stars":252},6,663,"4.x",4,1778593131000,10482,{"hasNpmPackage":241,"license":254,"smitheryVerified":241},"Apache-2.0",{"hasCi":256,"hasTests":241},true,{"updatedAt":258},1778691465826,{"basePath":260,"githubOwner":261,"githubRepo":262,"locale":18,"slug":263,"type":264},"skills/transformers-js","huggingface","skills","transformers-js","skill",{"_creationTime":266,"_id":267,"community":268,"display":269,"identity":274,"parentExtension":277,"providers":278,"relations":294,"tags":296,"workflow":297},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":270,"installMethods":271,"name":273,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":272},"huggingface-skills","Hugging Face Skills",{"basePath":275,"githubOwner":261,"githubRepo":262,"locale":18,"slug":262,"type":276},"","plugin",null,{"evaluate":279,"extract":289},{"promptVersionExtension":212,"promptVersionScoring":213,"score":280,"tags":281,"targetMarket":227,"tier":228},98,[261,282,283,284,285,286,287,288],"ai","ml","datasets","models","training","cli","python",{"commitSha":290,"license":254,"plugin":291},"HEAD",{"mcpCount":8,"provider":292,"skillCount":293},"classify",14,{"repoId":295},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[282,287,284,261,283,285,288,286],{"evaluatedAt":298,"extractAt":299,"updatedAt":298},1778691185872,1778690773482,{"evaluate":301,"extract":303},{"promptVersionExtension":212,"promptVersionScoring":213,"score":216,"tags":302,"targetMarket":227,"tier":228},[219,220,221,222,223,224,225,226],{"commitSha":290,"license":254},{"parentExtensionId":267,"repoId":295},{"_creationTime":306,"_id":295,"identity":307,"providers":308,"workflow":747},1778689536128.5474,{"githubOwner":261,"githubRepo":262,"sourceUrl":14},{"classify":309,"discover":740,"github":743},{"commitSha":290,"extensions":310},[311,325,334,342,350,358,366,374,382,390,398,406,414,420,428,436,479,487,493,499,516,522,529,571,582,601,607,627,639,663,721],{"basePath":275,"description":270,"displayName":272,"installMethods":312,"rationale":313,"selectedPaths":314,"source":323,"sourceLanguage":18,"type":324},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[315,318,320],{"path":316,"priority":317},".claude-plugin/marketplace.json","mandatory",{"path":319,"priority":317},"README.md",{"path":321,"priority":322},"LICENSE","high","rule","marketplace",{"basePath":326,"description":327,"displayName":328,"installMethods":329,"rationale":330,"selectedPaths":331,"source":323,"sourceLanguage":18,"type":276},"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":328},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[332],{"path":333,"priority":322},"SKILL.md",{"basePath":335,"description":336,"displayName":337,"installMethods":338,"rationale":339,"selectedPaths":340,"source":323,"sourceLanguage":18,"type":276},"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":337},"inline plugin source from marketplace.json at skills/huggingface-local-models",[341],{"path":333,"priority":322},{"basePath":343,"description":344,"displayName":345,"installMethods":346,"rationale":347,"selectedPaths":348,"source":323,"sourceLanguage":18,"type":276},"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":345},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[349],{"path":333,"priority":322},{"basePath":351,"description":352,"displayName":353,"installMethods":354,"rationale":355,"selectedPaths":356,"source":323,"sourceLanguage":18,"type":276},"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":353},"inline plugin source from marketplace.json at skills/huggingface-papers",[357],{"path":333,"priority":322},{"basePath":359,"description":360,"displayName":361,"installMethods":362,"rationale":363,"selectedPaths":364,"source":323,"sourceLanguage":18,"type":276},"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":361},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[365],{"path":333,"priority":322},{"basePath":367,"description":368,"displayName":369,"installMethods":370,"rationale":371,"selectedPaths":372,"source":323,"sourceLanguage":18,"type":276},"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":369},"inline plugin source from marketplace.json at skills/huggingface-best",[373],{"path":333,"priority":322},{"basePath":375,"description":376,"displayName":377,"installMethods":378,"rationale":379,"selectedPaths":380,"source":323,"sourceLanguage":18,"type":276},"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":377},"inline plugin source from marketplace.json at skills/hf-cli",[381],{"path":333,"priority":322},{"basePath":383,"description":384,"displayName":385,"installMethods":386,"rationale":387,"selectedPaths":388,"source":323,"sourceLanguage":18,"type":276},"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":385},"inline plugin source from marketplace.json at skills/huggingface-trackio",[389],{"path":333,"priority":322},{"basePath":391,"description":392,"displayName":393,"installMethods":394,"rationale":395,"selectedPaths":396,"source":323,"sourceLanguage":18,"type":276},"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":393},"inline plugin source from marketplace.json at skills/huggingface-datasets",[397],{"path":333,"priority":322},{"basePath":399,"description":400,"displayName":401,"installMethods":402,"rationale":403,"selectedPaths":404,"source":323,"sourceLanguage":18,"type":276},"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":401},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[405],{"path":333,"priority":322},{"basePath":407,"description":408,"displayName":409,"installMethods":410,"rationale":411,"selectedPaths":412,"source":323,"sourceLanguage":18,"type":276},"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":409},"inline plugin source from marketplace.json at skills/huggingface-gradio",[413],{"path":333,"priority":322},{"basePath":260,"description":415,"displayName":263,"installMethods":416,"rationale":417,"selectedPaths":418,"source":323,"sourceLanguage":18,"type":276},"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.",{"claudeCode":263},"inline plugin source from marketplace.json at skills/transformers-js",[419],{"path":333,"priority":322},{"basePath":421,"description":422,"displayName":423,"installMethods":424,"rationale":425,"selectedPaths":426,"source":323,"sourceLanguage":18,"type":276},"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":423},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[427],{"path":333,"priority":322},{"basePath":429,"description":430,"displayName":431,"installMethods":432,"rationale":433,"selectedPaths":434,"source":323,"sourceLanguage":18,"type":276},"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":431},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[435],{"path":333,"priority":322},{"basePath":275,"description":270,"displayName":272,"installMethods":437,"license":254,"rationale":438,"selectedPaths":439,"source":323,"sourceLanguage":18,"type":276},{"claudeCode":272},"plugin manifest at .claude-plugin/plugin.json",[440,442,443,444,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477],{"path":441,"priority":317},".claude-plugin/plugin.json",{"path":319,"priority":317},{"path":321,"priority":322},{"path":445,"priority":446},"skills/hf-cli/SKILL.md","medium",{"path":448,"priority":446},"skills/huggingface-best/SKILL.md",{"path":450,"priority":446},"skills/huggingface-community-evals/SKILL.md",{"path":452,"priority":446},"skills/huggingface-datasets/SKILL.md",{"path":454,"priority":446},"skills/huggingface-gradio/SKILL.md",{"path":456,"priority":446},"skills/huggingface-llm-trainer/SKILL.md",{"path":458,"priority":446},"skills/huggingface-local-models/SKILL.md",{"path":460,"priority":446},"skills/huggingface-paper-publisher/SKILL.md",{"path":462,"priority":446},"skills/huggingface-papers/SKILL.md",{"path":464,"priority":446},"skills/huggingface-tool-builder/SKILL.md",{"path":466,"priority":446},"skills/huggingface-trackio/SKILL.md",{"path":468,"priority":446},"skills/huggingface-vision-trainer/SKILL.md",{"path":470,"priority":446},"skills/train-sentence-transformers/SKILL.md",{"path":472,"priority":446},"skills/transformers-js/SKILL.md",{"path":474,"priority":317},".mcp.json",{"path":476,"priority":322},"agents/AGENTS.md",{"path":478,"priority":322},".cursor-plugin/plugin.json",{"basePath":480,"description":481,"displayName":482,"installMethods":483,"rationale":484,"selectedPaths":485,"source":323,"sourceLanguage":18,"type":264},"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",[486],{"path":333,"priority":317},{"basePath":375,"description":488,"displayName":377,"installMethods":489,"rationale":490,"selectedPaths":491,"source":323,"sourceLanguage":18,"type":264},"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},"SKILL.md frontmatter at skills/hf-cli/SKILL.md",[492],{"path":333,"priority":317},{"basePath":367,"description":494,"displayName":369,"installMethods":495,"rationale":496,"selectedPaths":497,"source":323,"sourceLanguage":18,"type":264},"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",[498],{"path":333,"priority":317},{"basePath":359,"description":500,"displayName":361,"installMethods":501,"rationale":502,"selectedPaths":503,"source":323,"sourceLanguage":18,"type":264},"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",[504,505,508,510,512,514],{"path":333,"priority":317},{"path":506,"priority":507},"examples/.env.example","low",{"path":509,"priority":507},"examples/USAGE_EXAMPLES.md",{"path":511,"priority":507},"scripts/inspect_eval_uv.py",{"path":513,"priority":507},"scripts/inspect_vllm_uv.py",{"path":515,"priority":507},"scripts/lighteval_vllm_uv.py",{"basePath":391,"description":517,"displayName":393,"installMethods":518,"rationale":519,"selectedPaths":520,"source":323,"sourceLanguage":18,"type":264},"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",[521],{"path":333,"priority":317},{"basePath":407,"description":408,"displayName":409,"installMethods":523,"rationale":524,"selectedPaths":525,"source":323,"sourceLanguage":18,"type":264},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[526,527],{"path":333,"priority":317},{"path":528,"priority":446},"examples.md",{"basePath":326,"description":530,"displayName":328,"installMethods":531,"rationale":532,"selectedPaths":533,"source":323,"sourceLanguage":18,"type":264},"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",[534,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569],{"path":333,"priority":317},{"path":536,"priority":446},"references/gguf_conversion.md",{"path":538,"priority":446},"references/hardware_guide.md",{"path":540,"priority":446},"references/hub_saving.md",{"path":542,"priority":446},"references/local_training_macos.md",{"path":544,"priority":446},"references/reliability_principles.md",{"path":546,"priority":446},"references/trackio_guide.md",{"path":548,"priority":446},"references/training_methods.md",{"path":550,"priority":446},"references/training_patterns.md",{"path":552,"priority":446},"references/troubleshooting.md",{"path":554,"priority":446},"references/unsloth.md",{"path":556,"priority":507},"scripts/convert_to_gguf.py",{"path":558,"priority":507},"scripts/dataset_inspector.py",{"path":560,"priority":507},"scripts/estimate_cost.py",{"path":562,"priority":507},"scripts/hf_benchmarks.py",{"path":564,"priority":507},"scripts/train_dpo_example.py",{"path":566,"priority":507},"scripts/train_grpo_example.py",{"path":568,"priority":507},"scripts/train_sft_example.py",{"path":570,"priority":507},"scripts/unsloth_sft_example.py",{"basePath":335,"description":336,"displayName":337,"installMethods":572,"rationale":573,"selectedPaths":574,"source":323,"sourceLanguage":18,"type":264},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[575,576,578,580],{"path":333,"priority":317},{"path":577,"priority":446},"references/hardware.md",{"path":579,"priority":446},"references/hub-discovery.md",{"path":581,"priority":446},"references/quantization.md",{"basePath":343,"description":344,"displayName":345,"installMethods":583,"rationale":584,"selectedPaths":585,"source":323,"sourceLanguage":18,"type":264},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[586,587,589,591,593,595,597,599],{"path":333,"priority":317},{"path":588,"priority":507},"examples/example_usage.md",{"path":590,"priority":446},"references/quick_reference.md",{"path":592,"priority":507},"scripts/paper_manager.py",{"path":594,"priority":507},"templates/arxiv.md",{"path":596,"priority":507},"templates/ml-report.md",{"path":598,"priority":507},"templates/modern.md",{"path":600,"priority":507},"templates/standard.md",{"basePath":351,"description":602,"displayName":353,"installMethods":603,"rationale":604,"selectedPaths":605,"source":323,"sourceLanguage":18,"type":264},"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",[606],{"path":333,"priority":317},{"basePath":399,"description":608,"displayName":401,"installMethods":609,"rationale":610,"selectedPaths":611,"source":323,"sourceLanguage":18,"type":264},"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",[612,613,615,617,619,621,623,625],{"path":333,"priority":317},{"path":614,"priority":446},"references/baseline_hf_api.py",{"path":616,"priority":446},"references/baseline_hf_api.sh",{"path":618,"priority":446},"references/baseline_hf_api.tsx",{"path":620,"priority":446},"references/find_models_by_paper.sh",{"path":622,"priority":446},"references/hf_enrich_models.sh",{"path":624,"priority":446},"references/hf_model_card_frontmatter.sh",{"path":626,"priority":446},"references/hf_model_papers_auth.sh",{"basePath":383,"description":628,"displayName":385,"installMethods":629,"rationale":630,"selectedPaths":631,"source":323,"sourceLanguage":18,"type":264},"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",[632,633,635,637],{"path":333,"priority":317},{"path":634,"priority":446},"references/alerts.md",{"path":636,"priority":446},"references/logging_metrics.md",{"path":638,"priority":446},"references/retrieving_metrics.md",{"basePath":421,"description":640,"displayName":423,"installMethods":641,"rationale":642,"selectedPaths":643,"source":323,"sourceLanguage":18,"type":264},"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",[644,645,647,648,650,652,653,655,656,657,659,661],{"path":333,"priority":317},{"path":646,"priority":446},"references/finetune_sam2_trainer.md",{"path":540,"priority":446},{"path":649,"priority":446},"references/image_classification_training_notebook.md",{"path":651,"priority":446},"references/object_detection_training_notebook.md",{"path":544,"priority":446},{"path":654,"priority":446},"references/timm_trainer.md",{"path":558,"priority":507},{"path":560,"priority":507},{"path":658,"priority":507},"scripts/image_classification_training.py",{"path":660,"priority":507},"scripts/object_detection_training.py",{"path":662,"priority":507},"scripts/sam_segmentation_training.py",{"basePath":429,"description":664,"displayName":431,"installMethods":665,"rationale":666,"selectedPaths":667,"source":323,"sourceLanguage":18,"type":264},"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",[668,669,671,673,675,677,679,680,682,684,686,688,690,692,694,695,697,699,701,703,705,707,709,711,713,715,717,719],{"path":333,"priority":317},{"path":670,"priority":446},"references/base_model_selection.md",{"path":672,"priority":446},"references/dataset_formats.md",{"path":674,"priority":446},"references/evaluators_cross_encoder.md",{"path":676,"priority":446},"references/evaluators_sentence_transformer.md",{"path":678,"priority":446},"references/evaluators_sparse_encoder.md",{"path":538,"priority":446},{"path":681,"priority":446},"references/hf_jobs_execution.md",{"path":683,"priority":446},"references/losses_cross_encoder.md",{"path":685,"priority":446},"references/losses_sentence_transformer.md",{"path":687,"priority":446},"references/losses_sparse_encoder.md",{"path":689,"priority":446},"references/model_architectures.md",{"path":691,"priority":446},"references/prompts_and_instructions.md",{"path":693,"priority":446},"references/training_args.md",{"path":552,"priority":446},{"path":696,"priority":507},"scripts/mine_hard_negatives.py",{"path":698,"priority":507},"scripts/train_cross_encoder_distillation_example.py",{"path":700,"priority":507},"scripts/train_cross_encoder_example.py",{"path":702,"priority":507},"scripts/train_cross_encoder_listwise_example.py",{"path":704,"priority":507},"scripts/train_sentence_transformer_distillation_example.py",{"path":706,"priority":507},"scripts/train_sentence_transformer_example.py",{"path":708,"priority":507},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":710,"priority":507},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":712,"priority":507},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":714,"priority":507},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":716,"priority":507},"scripts/train_sentence_transformer_with_lora_example.py",{"path":718,"priority":507},"scripts/train_sparse_encoder_distillation_example.py",{"path":720,"priority":507},"scripts/train_sparse_encoder_example.py",{"basePath":260,"description":10,"displayName":263,"installMethods":722,"rationale":723,"selectedPaths":724,"source":323,"sourceLanguage":18,"type":264},{"claudeCode":12},"SKILL.md frontmatter at skills/transformers-js/SKILL.md",[725,726,728,730,732,734,736,738],{"path":333,"priority":317},{"path":727,"priority":446},"references/CACHE.md",{"path":729,"priority":446},"references/CONFIGURATION.md",{"path":731,"priority":446},"references/EXAMPLES.md",{"path":733,"priority":446},"references/MODEL_ARCHITECTURES.md",{"path":735,"priority":446},"references/MODEL_REGISTRY.md",{"path":737,"priority":446},"references/PIPELINE_OPTIONS.md",{"path":739,"priority":446},"references/TEXT_GENERATION.md",{"sources":741},[742],"manual",{"closedIssues90d":247,"description":744,"forks":248,"homepage":745,"license":254,"openIssues90d":250,"pushedAt":251,"readmeSize":245,"stars":252,"topics":746},"Give your agents the power of the Hugging Face ecosystem","https://huggingface.co",[],{"classifiedAt":748,"discoverAt":749,"extractAt":750,"githubAt":750,"updatedAt":748},1778690772996,1778689536128,1778690770714,[224,223,220,219,222,221,226,225],{"evaluatedAt":258,"extractAt":299,"updatedAt":258},[],[755,784,813,844,872,903],{"_creationTime":756,"_id":757,"community":758,"display":759,"identity":765,"providers":769,"relations":777,"tags":780,"workflow":781},1778699234184.6133,"k170q6m14w6ah5ygc0jr5sa54986mpx7",{"reviewCount":8},{"description":760,"installMethods":761,"name":763,"sourceUrl":764},"Deep codebase initialization with hierarchical AGENTS.md documentation",{"claudeCode":762},"Yeachan-Heo/oh-my-claudecode","deepinit","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":766,"githubOwner":767,"githubRepo":768,"locale":18,"slug":763,"type":264},"skills/deepinit","Yeachan-Heo","oh-my-claudecode",{"evaluate":770,"extract":776},{"promptVersionExtension":212,"promptVersionScoring":213,"score":771,"tags":772,"targetMarket":227,"tier":228},100,[773,774,775,221,220],"documentation","codebase","agent",{"commitSha":290},{"parentExtensionId":778,"repoId":779},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[775,774,773,220,221],{"evaluatedAt":782,"extractAt":783,"updatedAt":782},1778699437749,1778699234184,{"_creationTime":785,"_id":786,"community":787,"display":788,"identity":794,"providers":798,"relations":807,"tags":809,"workflow":810},1778696052276.0203,"k17bgxxgryq8edg32egypsvqtn86m1h7",{"reviewCount":8},{"description":789,"installMethods":790,"name":792,"sourceUrl":793},"Detect and untangle circular dependencies. Runs madge/skott (TS), pycycle (Py), or compiler-only checks (Go/Rust). Auto-fixes leaf-extractable cycles; reports core cycles for human review. Use when the user asks to find circular imports, fix dependency cycles, or untangle module graph. Example queries — \"find circular imports\", \"fix dependency cycles\", \"untangle our module graph\", \"why is madge complaining\".",{"claudeCode":791},"raintree-technology/claude-starter","cleanup-cycles","https://github.com/raintree-technology/claude-starter",{"basePath":795,"githubOwner":796,"githubRepo":797,"locale":18,"slug":792,"type":264},"templates/.claude/skills/cleanup-cycles","raintree-technology","claude-starter",{"evaluate":799,"extract":806},{"promptVersionExtension":212,"promptVersionScoring":213,"score":771,"tags":800,"targetMarket":227,"tier":228},[801,802,220,288,221,803,804,805],"code-quality","dependencies","go","rust","refactoring",{"commitSha":290},{"repoId":808},"kd78ywakatnz4sjfx781sy14vh86mtty",[801,802,803,220,288,805,804,221],{"evaluatedAt":811,"extractAt":812,"updatedAt":811},1778696977114,1778696052276,{"_creationTime":814,"_id":815,"community":816,"display":817,"identity":823,"providers":828,"relations":835,"tags":839,"workflow":840},1778694990914.8232,"k170mmr549jkqghjyp3y2gxcr186nh6y",{"reviewCount":8},{"description":818,"installMethods":819,"name":821,"sourceUrl":822},"用于身份验证、用户注册、登录、密码恢复、OAuth 提供商、基于角色的访问控制或保护路由和函数。始终使用 `@netlify/identity`。切勿使用 `netlify-identity-widget` 或 `gotrue-js` — 它们已弃用。",{"claudeCode":820},"netlify/context-and-tools","netlify-identity","https://github.com/netlify/context-and-tools",{"basePath":824,"githubOwner":825,"githubRepo":826,"locale":827,"slug":821,"type":264},"skills/netlify-identity","netlify","context-and-tools","zh-CN",{"evaluate":829,"extract":834},{"promptVersionExtension":212,"promptVersionScoring":213,"score":771,"tags":830,"targetMarket":227,"tier":228},[831,825,832,220,221,833],"authentication","identity","api",{"commitSha":290},{"parentExtensionId":836,"repoId":837,"translatedFrom":838},"k1714spp30a0rvg5y3yjga772n86nmps","kd7b1ncy2zzzfws29grdt8heb986ntzq","k17f1596a2t00btq1hfksssg0s86n6ej",[833,831,832,220,825,221],{"evaluatedAt":841,"extractAt":842,"updatedAt":843},1778694839805,1778694599571,1778694990914,{"_creationTime":845,"_id":846,"community":847,"display":848,"identity":854,"providers":858,"relations":865,"tags":868,"workflow":869},1778690831986.3765,"k179x509d3fng6rhce5txz8grx86m5mw",{"reviewCount":8},{"description":849,"installMethods":850,"name":852,"sourceUrl":853},"Vue Router 4 patterns, navigation guards, route params, and route-component lifecycle interactions.",{"claudeCode":851},"hyf0/vue-skills","vue-router-best-practices","https://github.com/hyf0/vue-skills",{"basePath":855,"githubOwner":856,"githubRepo":857,"locale":18,"slug":852,"type":264},"skills/vue-router-best-practices","hyf0","vue-skills",{"evaluate":859,"extract":864},{"promptVersionExtension":212,"promptVersionScoring":213,"score":771,"tags":860,"targetMarket":227,"tier":228},[861,862,220,221,863],"vue","vue-router","web-development",{"commitSha":290},{"parentExtensionId":866,"repoId":867},"k17fvvpt61wrah7aepwqhgjp4d86n8jx","kd7a1a0bdc2ez150x3razht61n86m6a8",[220,221,861,862,863],{"evaluatedAt":870,"extractAt":871,"updatedAt":870},1778691174475,1778690831986,{"_creationTime":873,"_id":874,"community":875,"display":876,"identity":882,"providers":886,"relations":896,"tags":899,"workflow":900},1778687299685.3901,"k1766avpz66czpkss71dc79vq586nf9j",{"reviewCount":8},{"description":877,"installMethods":878,"name":880,"sourceUrl":881},"Full Sentry SDK setup for React Router Framework mode. Use when asked to \"add Sentry to React Router Framework\", \"install @sentry/react-router\", or configure error monitoring, tracing, profiling, session replay, logs, or user feedback for a React Router v7 framework app.",{"claudeCode":879},"getsentry/sentry-for-ai","sentry-react-router-framework-sdk","https://github.com/getsentry/sentry-for-ai",{"basePath":883,"githubOwner":884,"githubRepo":885,"locale":18,"slug":880,"type":264},"skills/sentry-react-router-framework-sdk","getsentry","sentry-for-ai",{"evaluate":887,"extract":895},{"promptVersionExtension":212,"promptVersionScoring":213,"score":771,"tags":888,"targetMarket":227,"tier":228},[889,890,891,892,893,894,221,220],"react","sentry","sdk-setup","error-monitoring","tracing","profiling",{"commitSha":290},{"parentExtensionId":897,"repoId":898},"k179krjesmjphb7bqfh43701sn86n8mn","kd72wxwjk5zaddkehkc2ftrzfh86nk3n",[892,220,894,889,891,890,893,221],{"evaluatedAt":901,"extractAt":902,"updatedAt":901},1778687838591,1778687299685,{"_creationTime":904,"_id":905,"community":906,"display":907,"identity":913,"providers":918,"relations":924,"tags":926,"workflow":927},1778691799740.4983,"k1797kqt7c6p7gytn30rckwzvh86nz20",{"reviewCount":8},{"description":908,"installMethods":909,"name":911,"sourceUrl":912},"This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.",{"claudeCode":910},"K-Dense-AI/claude-scientific-skills","Transformers","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":914,"githubOwner":915,"githubRepo":916,"locale":18,"slug":917,"type":264},"scientific-skills/transformers","K-Dense-AI","claude-scientific-skills","transformers",{"evaluate":919,"extract":923},{"promptVersionExtension":212,"promptVersionScoring":213,"score":280,"tags":920,"targetMarket":227,"tier":228},[222,223,224,921,219,922,917,261],"multimodal","deep-learning",{"commitSha":290,"license":254},{"repoId":925},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[224,223,922,261,219,921,222,917],{"evaluatedAt":928,"extractAt":929,"updatedAt":928},1778694649795,1778691799740]