[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-huggingface-huggingface-tool-builder-zh-CN":3,"guides-for-huggingface-huggingface-tool-builder":743,"similar-k1726ndq21b032g066r345zd6s86m3d9-zh-CN":744},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":251,"isFallback":248,"parentExtension":256,"providers":290,"relations":294,"repo":295,"tags":741,"workflow":742},1778690773482.4888,"k1726ndq21b032g066r345zd6s86m3d9",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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},"huggingface/skills","Hugging Face Tool Builder","https://github.com/huggingface/skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":232,"workflow":249},1778691398007.7212,"kn7b8fcc03ft878dawmfznafe186nrak","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"practices":204,"prerequisites":205,"promptVersionExtension":206,"promptVersionScoring":207,"purpose":208,"rationale":209,"score":210,"summary":211,"tags":212,"targetMarket":218,"tier":219,"useCases":220,"workflow":225},[21,26,29,32,36,39,44,48,51,54,58,62,65,69,72,75,78,81,84,87,91,95,99,103,107,110,114,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem: building reusable scripts for Hugging Face API tasks, especially for repeated or automated actions, and mentions specific use cases like chaining API calls and enriching data.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers value beyond simple API calls by focusing on creating reusable scripts, handling authentication hygienically, parsing complex outputs, and demonstrating composability through piping and chaining, which is more than a thin wrapper.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides multiple example scripts covering different use cases (specific models, trending models, frontmatter extraction, baseline API calls) and demonstrates robust error handling and authentication setup, indicating it's ready for real workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on building scripts for Hugging Face API and CLI workflows, encompassing related tasks like data fetching, enrichment, and processing, all within a coherent domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's purpose of building reusable scripts for Hugging Face API tasks and automation.",{"category":40,"check":41,"severity":42,"summary":43},"Invocation","Scoped tools","not_applicable","This skill primarily uses shell scripts and direct API calls rather than exposing distinct, scoped tools. The scripts themselves are the 'tools'.",{"category":45,"check":46,"severity":24,"summary":47},"Documentation","Configuration & parameter reference","The script examples and help text clearly document input parameters (model ID, limit, --token flag) and the usage of the HF_TOKEN environment variable for authentication.",{"category":33,"check":49,"severity":42,"summary":50},"Tool naming","This skill does not expose named tools; it relies on executing scripts and potentially using the 'hf' CLI which has its own command structure.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The scripts are designed to take specific arguments (model IDs, limits) or read from stdin, and their outputs are generally structured JSON or clear text, appropriate for their task.",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The extension is licensed under the Apache-2.0 license, which is permissive and widely usable.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The latest commit was on 2026-05-12, which is within the last 3 months.",{"category":59,"check":63,"severity":24,"summary":64},"Dependency Management","The provided scripts rely on common CLI tools like `curl`, `jq`, `python3`, and `hf`, which are standard and generally maintained.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","Secrets (HF_TOKEN) are handled via environment variables and documented for use in API calls, with no evidence of secrets being hardcoded or leaked in output.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The scripts process data from the Hugging Face API and user input (model IDs) but do not execute arbitrary code based on external data. Input parameters like model IDs are used in API calls or passed to other tools, not executed directly as commands.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill relies on the 'hf' CLI and standard shell utilities, which are assumed to be part of the execution environment. It does not fetch or execute arbitrary code at runtime.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The scripts operate within the expected scope of interacting with APIs and local file system for temporary storage (e.g., downloaded READMEs), without evidence of attempting to modify files outside their designated project area.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached process spawns (e.g., `nohup`, `&` without proper handling) or retry loops around denied tool calls were found in the provided scripts.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The scripts interact with the Hugging Face API and do not exhibit any behavior that suggests exfiltration of confidential data or undocumented outbound calls.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled scripts and SKILL.md file do not contain any hidden text, control characters, or Unicode tricks that could mislead the model.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The scripts are written in plain shell, Python, and TypeScript, and are not obfuscated using methods like base64 encoding or runtime code fetching.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The scripts correctly handle paths by using relative paths for downloaded files or relying on standard CLI tool outputs, and do not make assumptions about the user's project structure beyond the immediate task.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","The repository has 4 open issues and 6 closed issues in the last 90 days, indicating active maintenance with a reasonable closure rate.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The repository uses Git commits for versioning, and the 'pushedAt' date indicates recent activity. The lack of explicit versioning in frontmatter or tags is mitigated by the direct use of the repo.",{"category":104,"check":105,"severity":24,"summary":106},"Execution","Validation","Input parameters like model IDs and limits are checked for validity (e.g., is digit, not empty). API responses are parsed with `jq` or Python's JSON library, which handle malformed JSON gracefully.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The scripts are primarily read-only, focusing on fetching data and metadata. No destructive operations like file deletion or modification are performed.",{"category":111,"check":112,"severity":24,"summary":113},"Code Execution","Error Handling","Scripts use `set -e` and explicit checks for command success (e.g., `curl` exit codes, `jq` parsing) and provide user-friendly error messages, exiting non-zero on failure.",{"category":111,"check":115,"severity":42,"summary":116},"Logging","These scripts are designed for CLI execution and do not inherently require a separate audit log file. Their output is standard stdout/stderr, which can be captured by the user.",{"category":118,"check":119,"severity":42,"summary":120},"Compliance","GDPR","The skill primarily interacts with public Hugging Face API endpoints for model metadata and does not appear to handle personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill operates on Hugging Face API endpoints and standard CLI tools, with no regional or jurisdictional logic detected, making it globally applicable.",{"category":92,"check":125,"severity":24,"summary":126},"Runtime stability","The scripts rely on common CLI tools (`curl`, `jq`, `python3`, `hf`) and standard shell features, making them portable across POSIX-compliant systems.",{"category":45,"check":128,"severity":24,"summary":129},"README","The `README.md` file for the Hugging Face Skills repository provides a good overview and installation instructions, and the specific skill's SKILL.md has a clear description.",{"category":33,"check":131,"severity":42,"summary":132},"Tool surface size","This extension is a collection of scripts rather than a single tool with a defined set of commands. The complexity is managed by the script examples themselves.",{"category":40,"check":134,"severity":42,"summary":135},"Overlapping near-synonym tools","This skill consists of individual scripts, each with a distinct purpose, rather than exposing multiple tools with overlapping functionality.",{"category":45,"check":137,"severity":24,"summary":138},"Phantom features","All advertised capabilities, such as fetching model metadata, parsing frontmatter, and handling authentication, are implemented in the provided scripts.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The main README provides clear installation instructions for various agents (Claude Code, Codex, Gemini CLI, Cursor), and the SKILL.md includes usage examples and prerequisites.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The scripts provide clear error messages indicating what failed (e.g., download failed, invalid JSON, model not found) and often suggest next steps or provide usage information.",{"category":104,"check":148,"severity":24,"summary":149},"Pinned dependencies","The scripts rely on common system tools (`curl`, `jq`, `python3`) and the `hf` CLI, which are expected to be installed and managed by the user's environment. Python scripts are runnable with standard Python 3.",{"category":33,"check":151,"severity":42,"summary":152},"Dry-run preview","The skill is primarily read-only, fetching data from the Hugging Face API. There are no state-changing operations that would require a dry-run mode.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The scripts use `curl` which has built-in retry mechanisms and timeouts. API calls are generally idempotent as they are read operations. Error handling accounts for potential network issues.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The scripts do not emit any telemetry. Any usage is dictated by the user running the scripts.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly states the purpose is to build reusable scripts for Hugging Face API workflows and provides specific triggers like 'when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help'.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter in SKILL.md is concise and effectively summarizes the skill's core capability and use cases.",{"category":45,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is reasonably concise, with detailed examples and API information provided in separate reference files, following progressive disclosure.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Detailed examples and API information are provided in the `references/` directory, linked from the main SKILL.md file, demonstrating effective progressive disclosure.",{"category":170,"check":174,"severity":42,"summary":175},"Forked exploration","This skill is not an exploration or audit-style skill that requires forked context; it performs specific, direct tasks.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides multiple, ready-to-use examples for various scripts, demonstrating input, invocation, and expected output formats.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The scripts handle edge cases such as missing inputs, API errors, and download failures, providing informative error messages and fallback behaviors.",{"category":111,"check":183,"severity":42,"summary":184},"Tool Fallback","This skill relies on external CLIs like `hf` and standard utilities. It does not depend on a specific version of a custom MCP server with a fallback path.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The scripts are designed to halt execution on errors using `set -e` and explicit checks, preventing unexpected state from persisting or causing further issues.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not implicitly rely on other skills. Its documentation guides users on how to use it with other tools like `jq` and other Hugging Face skills.",1778691397128,"This skill provides a collection of shell, Python, and TypeScript scripts designed to interact with the Hugging Face API and CLI. It focuses on creating reusable tools for fetching, processing, and automating tasks involving Hugging Face data and models, including handling authentication and parsing complex API responses.",[195,196,197,198,199],"Create reusable scripts for Hugging Face API and CLI","Fetch and process Hugging Face data and model metadata","Automate repeated tasks and API chaining","Handle authentication with HF_TOKEN","Provide baseline scripts and enriched utilities",[201,202,203],"Performing complex model training or fine-tuning.","Directly managing Hugging Face infrastructure beyond CLI interactions.","Replacing the core functionality of the Hugging Face Hub website.",[],[],"3.0.0","4.4.0","Build reusable scripts and tools for Hugging Face API and CLI workflows, enabling automation and composability of tasks.","The extension is highly polished with comprehensive documentation, robust error handling, and excellent examples. No critical or warning findings were observed.",98,"A high-quality skill for building reusable Hugging Face API scripts.",[213,214,215,216,217],"huggingface","api","scripting","automation","data-processing","global","verified",[221,222,223,224],"When you need to build a script to automate fetching model information.","When you want to combine Hugging Face API calls for a specific task.","When you need to process or enrich Hugging Face dataset or model metadata.","When creating repeatable workflows involving Hugging Face Hub resources.",[226,227,228,229,230,231],"User specifies a task or data to process from Hugging Face.","Skill generates or selects an appropriate script (Bash, Python, TSX).","Script fetches data from Hugging Face API or uses `hf` CLI.","Script processes, enriches, or formats the data as requested.","Script outputs results to stdout or a file, or executes a task.","User reviews output or script's outcome.",{"codeQuality":233,"collectedAt":235,"documentation":236,"maintenance":239,"security":245,"testCoverage":247},{"hasLockfile":234},false,1778691370000,{"descriptionLength":237,"readmeSize":238},303,9821,{"closedIssues90d":240,"forks":241,"hasChangelog":234,"openIssues90d":242,"pushedAt":243,"stars":244},6,663,4,1778593131000,10482,{"hasNpmPackage":234,"license":246,"smitheryVerified":234},"Apache-2.0",{"hasCi":248,"hasTests":234},true,{"updatedAt":250},1778691398007,{"basePath":252,"githubOwner":213,"githubRepo":253,"locale":18,"slug":254,"type":255},"skills/huggingface-tool-builder","skills","huggingface-tool-builder","skill",{"_creationTime":257,"_id":258,"community":259,"display":260,"identity":265,"parentExtension":268,"providers":269,"relations":284,"tags":286,"workflow":287},1778690773482.486,"k175g1spb5757qt4tnj9cktcn986mshy",{"reviewCount":8},{"description":261,"installMethods":262,"name":264,"sourceUrl":14},"Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub",{"claudeCode":263},"huggingface-skills","Hugging Face Skills",{"basePath":266,"githubOwner":213,"githubRepo":253,"locale":18,"slug":253,"type":267},"","plugin",null,{"evaluate":270,"extract":279},{"promptVersionExtension":206,"promptVersionScoring":207,"score":210,"tags":271,"targetMarket":218,"tier":219},[213,272,273,274,275,276,277,278],"ai","ml","datasets","models","training","cli","python",{"commitSha":280,"license":246,"plugin":281},"HEAD",{"mcpCount":8,"provider":282,"skillCount":283},"classify",14,{"repoId":285},"kd72xwt5xnc0ktc4p7smzfcp3986m959",[272,277,274,213,273,275,278,276],{"evaluatedAt":288,"extractAt":289,"updatedAt":288},1778691185872,1778690773482,{"evaluate":291,"extract":293},{"promptVersionExtension":206,"promptVersionScoring":207,"score":210,"tags":292,"targetMarket":218,"tier":219},[213,214,215,216,217],{"commitSha":280,"license":246},{"parentExtensionId":258,"repoId":285},{"_creationTime":296,"_id":285,"identity":297,"providers":298,"workflow":737},1778689536128.5474,{"githubOwner":213,"githubRepo":253,"sourceUrl":14},{"classify":299,"discover":730,"github":733},{"commitSha":280,"extensions":300},[301,315,324,332,340,348,356,364,372,380,388,394,402,410,418,426,469,477,483,489,506,512,519,561,572,591,597,616,628,652,710],{"basePath":266,"description":261,"displayName":263,"installMethods":302,"rationale":303,"selectedPaths":304,"source":313,"sourceLanguage":18,"type":314},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[305,308,310],{"path":306,"priority":307},".claude-plugin/marketplace.json","mandatory",{"path":309,"priority":307},"README.md",{"path":311,"priority":312},"LICENSE","high","rule","marketplace",{"basePath":316,"description":317,"displayName":318,"installMethods":319,"rationale":320,"selectedPaths":321,"source":313,"sourceLanguage":18,"type":267},"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":318},"inline plugin source from marketplace.json at skills/huggingface-llm-trainer",[322],{"path":323,"priority":312},"SKILL.md",{"basePath":325,"description":326,"displayName":327,"installMethods":328,"rationale":329,"selectedPaths":330,"source":313,"sourceLanguage":18,"type":267},"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":327},"inline plugin source from marketplace.json at skills/huggingface-local-models",[331],{"path":323,"priority":312},{"basePath":333,"description":334,"displayName":335,"installMethods":336,"rationale":337,"selectedPaths":338,"source":313,"sourceLanguage":18,"type":267},"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":335},"inline plugin source from marketplace.json at skills/huggingface-paper-publisher",[339],{"path":323,"priority":312},{"basePath":341,"description":342,"displayName":343,"installMethods":344,"rationale":345,"selectedPaths":346,"source":313,"sourceLanguage":18,"type":267},"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":343},"inline plugin source from marketplace.json at skills/huggingface-papers",[347],{"path":323,"priority":312},{"basePath":349,"description":350,"displayName":351,"installMethods":352,"rationale":353,"selectedPaths":354,"source":313,"sourceLanguage":18,"type":267},"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":351},"inline plugin source from marketplace.json at skills/huggingface-community-evals",[355],{"path":323,"priority":312},{"basePath":357,"description":358,"displayName":359,"installMethods":360,"rationale":361,"selectedPaths":362,"source":313,"sourceLanguage":18,"type":267},"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":359},"inline plugin source from marketplace.json at skills/huggingface-best",[363],{"path":323,"priority":312},{"basePath":365,"description":366,"displayName":367,"installMethods":368,"rationale":369,"selectedPaths":370,"source":313,"sourceLanguage":18,"type":267},"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":367},"inline plugin source from marketplace.json at skills/hf-cli",[371],{"path":323,"priority":312},{"basePath":373,"description":374,"displayName":375,"installMethods":376,"rationale":377,"selectedPaths":378,"source":313,"sourceLanguage":18,"type":267},"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":375},"inline plugin source from marketplace.json at skills/huggingface-trackio",[379],{"path":323,"priority":312},{"basePath":381,"description":382,"displayName":383,"installMethods":384,"rationale":385,"selectedPaths":386,"source":313,"sourceLanguage":18,"type":267},"skills/huggingface-datasets","Explore, query, and extract data from any Hugging Face dataset using the Dataset Viewer REST API and npx tooling. Zero Python dependencies — covers split/config discovery, row pagination, text search, filtering, SQL via parquetlens, and dataset upload via CLI.","huggingface-datasets",{"claudeCode":383},"inline plugin source from marketplace.json at skills/huggingface-datasets",[387],{"path":323,"priority":312},{"basePath":252,"description":389,"displayName":254,"installMethods":390,"rationale":391,"selectedPaths":392,"source":313,"sourceLanguage":18,"type":267},"Build reusable scripts for Hugging Face Hub and API workflows. Useful for chaining API calls, enriching Hub metadata, or automating repeated tasks.",{"claudeCode":254},"inline plugin source from marketplace.json at skills/huggingface-tool-builder",[393],{"path":323,"priority":312},{"basePath":395,"description":396,"displayName":397,"installMethods":398,"rationale":399,"selectedPaths":400,"source":313,"sourceLanguage":18,"type":267},"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":397},"inline plugin source from marketplace.json at skills/huggingface-gradio",[401],{"path":323,"priority":312},{"basePath":403,"description":404,"displayName":405,"installMethods":406,"rationale":407,"selectedPaths":408,"source":313,"sourceLanguage":18,"type":267},"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":405},"inline plugin source from marketplace.json at skills/transformers-js",[409],{"path":323,"priority":312},{"basePath":411,"description":412,"displayName":413,"installMethods":414,"rationale":415,"selectedPaths":416,"source":313,"sourceLanguage":18,"type":267},"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":413},"inline plugin source from marketplace.json at skills/huggingface-vision-trainer",[417],{"path":323,"priority":312},{"basePath":419,"description":420,"displayName":421,"installMethods":422,"rationale":423,"selectedPaths":424,"source":313,"sourceLanguage":18,"type":267},"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":421},"inline plugin source from marketplace.json at skills/train-sentence-transformers",[425],{"path":323,"priority":312},{"basePath":266,"description":261,"displayName":263,"installMethods":427,"license":246,"rationale":428,"selectedPaths":429,"source":313,"sourceLanguage":18,"type":267},{"claudeCode":263},"plugin manifest at .claude-plugin/plugin.json",[430,432,433,434,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467],{"path":431,"priority":307},".claude-plugin/plugin.json",{"path":309,"priority":307},{"path":311,"priority":312},{"path":435,"priority":436},"skills/hf-cli/SKILL.md","medium",{"path":438,"priority":436},"skills/huggingface-best/SKILL.md",{"path":440,"priority":436},"skills/huggingface-community-evals/SKILL.md",{"path":442,"priority":436},"skills/huggingface-datasets/SKILL.md",{"path":444,"priority":436},"skills/huggingface-gradio/SKILL.md",{"path":446,"priority":436},"skills/huggingface-llm-trainer/SKILL.md",{"path":448,"priority":436},"skills/huggingface-local-models/SKILL.md",{"path":450,"priority":436},"skills/huggingface-paper-publisher/SKILL.md",{"path":452,"priority":436},"skills/huggingface-papers/SKILL.md",{"path":454,"priority":436},"skills/huggingface-tool-builder/SKILL.md",{"path":456,"priority":436},"skills/huggingface-trackio/SKILL.md",{"path":458,"priority":436},"skills/huggingface-vision-trainer/SKILL.md",{"path":460,"priority":436},"skills/train-sentence-transformers/SKILL.md",{"path":462,"priority":436},"skills/transformers-js/SKILL.md",{"path":464,"priority":307},".mcp.json",{"path":466,"priority":312},"agents/AGENTS.md",{"path":468,"priority":312},".cursor-plugin/plugin.json",{"basePath":470,"description":471,"displayName":472,"installMethods":473,"rationale":474,"selectedPaths":475,"source":313,"sourceLanguage":18,"type":255},"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",[476],{"path":323,"priority":307},{"basePath":365,"description":478,"displayName":367,"installMethods":479,"rationale":480,"selectedPaths":481,"source":313,"sourceLanguage":18,"type":255},"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",[482],{"path":323,"priority":307},{"basePath":357,"description":484,"displayName":359,"installMethods":485,"rationale":486,"selectedPaths":487,"source":313,"sourceLanguage":18,"type":255},"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",[488],{"path":323,"priority":307},{"basePath":349,"description":490,"displayName":351,"installMethods":491,"rationale":492,"selectedPaths":493,"source":313,"sourceLanguage":18,"type":255},"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",[494,495,498,500,502,504],{"path":323,"priority":307},{"path":496,"priority":497},"examples/.env.example","low",{"path":499,"priority":497},"examples/USAGE_EXAMPLES.md",{"path":501,"priority":497},"scripts/inspect_eval_uv.py",{"path":503,"priority":497},"scripts/inspect_vllm_uv.py",{"path":505,"priority":497},"scripts/lighteval_vllm_uv.py",{"basePath":381,"description":507,"displayName":383,"installMethods":508,"rationale":509,"selectedPaths":510,"source":313,"sourceLanguage":18,"type":255},"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",[511],{"path":323,"priority":307},{"basePath":395,"description":396,"displayName":397,"installMethods":513,"rationale":514,"selectedPaths":515,"source":313,"sourceLanguage":18,"type":255},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-gradio/SKILL.md",[516,517],{"path":323,"priority":307},{"path":518,"priority":436},"examples.md",{"basePath":316,"description":520,"displayName":318,"installMethods":521,"rationale":522,"selectedPaths":523,"source":313,"sourceLanguage":18,"type":255},"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",[524,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559],{"path":323,"priority":307},{"path":526,"priority":436},"references/gguf_conversion.md",{"path":528,"priority":436},"references/hardware_guide.md",{"path":530,"priority":436},"references/hub_saving.md",{"path":532,"priority":436},"references/local_training_macos.md",{"path":534,"priority":436},"references/reliability_principles.md",{"path":536,"priority":436},"references/trackio_guide.md",{"path":538,"priority":436},"references/training_methods.md",{"path":540,"priority":436},"references/training_patterns.md",{"path":542,"priority":436},"references/troubleshooting.md",{"path":544,"priority":436},"references/unsloth.md",{"path":546,"priority":497},"scripts/convert_to_gguf.py",{"path":548,"priority":497},"scripts/dataset_inspector.py",{"path":550,"priority":497},"scripts/estimate_cost.py",{"path":552,"priority":497},"scripts/hf_benchmarks.py",{"path":554,"priority":497},"scripts/train_dpo_example.py",{"path":556,"priority":497},"scripts/train_grpo_example.py",{"path":558,"priority":497},"scripts/train_sft_example.py",{"path":560,"priority":497},"scripts/unsloth_sft_example.py",{"basePath":325,"description":326,"displayName":327,"installMethods":562,"rationale":563,"selectedPaths":564,"source":313,"sourceLanguage":18,"type":255},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-local-models/SKILL.md",[565,566,568,570],{"path":323,"priority":307},{"path":567,"priority":436},"references/hardware.md",{"path":569,"priority":436},"references/hub-discovery.md",{"path":571,"priority":436},"references/quantization.md",{"basePath":333,"description":334,"displayName":335,"installMethods":573,"rationale":574,"selectedPaths":575,"source":313,"sourceLanguage":18,"type":255},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-paper-publisher/SKILL.md",[576,577,579,581,583,585,587,589],{"path":323,"priority":307},{"path":578,"priority":497},"examples/example_usage.md",{"path":580,"priority":436},"references/quick_reference.md",{"path":582,"priority":497},"scripts/paper_manager.py",{"path":584,"priority":497},"templates/arxiv.md",{"path":586,"priority":497},"templates/ml-report.md",{"path":588,"priority":497},"templates/modern.md",{"path":590,"priority":497},"templates/standard.md",{"basePath":341,"description":592,"displayName":343,"installMethods":593,"rationale":594,"selectedPaths":595,"source":313,"sourceLanguage":18,"type":255},"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",[596],{"path":323,"priority":307},{"basePath":252,"description":10,"displayName":254,"installMethods":598,"rationale":599,"selectedPaths":600,"source":313,"sourceLanguage":18,"type":255},{"claudeCode":12},"SKILL.md frontmatter at skills/huggingface-tool-builder/SKILL.md",[601,602,604,606,608,610,612,614],{"path":323,"priority":307},{"path":603,"priority":436},"references/baseline_hf_api.py",{"path":605,"priority":436},"references/baseline_hf_api.sh",{"path":607,"priority":436},"references/baseline_hf_api.tsx",{"path":609,"priority":436},"references/find_models_by_paper.sh",{"path":611,"priority":436},"references/hf_enrich_models.sh",{"path":613,"priority":436},"references/hf_model_card_frontmatter.sh",{"path":615,"priority":436},"references/hf_model_papers_auth.sh",{"basePath":373,"description":617,"displayName":375,"installMethods":618,"rationale":619,"selectedPaths":620,"source":313,"sourceLanguage":18,"type":255},"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",[621,622,624,626],{"path":323,"priority":307},{"path":623,"priority":436},"references/alerts.md",{"path":625,"priority":436},"references/logging_metrics.md",{"path":627,"priority":436},"references/retrieving_metrics.md",{"basePath":411,"description":629,"displayName":413,"installMethods":630,"rationale":631,"selectedPaths":632,"source":313,"sourceLanguage":18,"type":255},"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",[633,634,636,637,639,641,642,644,645,646,648,650],{"path":323,"priority":307},{"path":635,"priority":436},"references/finetune_sam2_trainer.md",{"path":530,"priority":436},{"path":638,"priority":436},"references/image_classification_training_notebook.md",{"path":640,"priority":436},"references/object_detection_training_notebook.md",{"path":534,"priority":436},{"path":643,"priority":436},"references/timm_trainer.md",{"path":548,"priority":497},{"path":550,"priority":497},{"path":647,"priority":497},"scripts/image_classification_training.py",{"path":649,"priority":497},"scripts/object_detection_training.py",{"path":651,"priority":497},"scripts/sam_segmentation_training.py",{"basePath":419,"description":653,"displayName":421,"installMethods":654,"rationale":655,"selectedPaths":656,"source":313,"sourceLanguage":18,"type":255},"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",[657,658,660,662,664,666,668,669,671,673,675,677,679,681,683,684,686,688,690,692,694,696,698,700,702,704,706,708],{"path":323,"priority":307},{"path":659,"priority":436},"references/base_model_selection.md",{"path":661,"priority":436},"references/dataset_formats.md",{"path":663,"priority":436},"references/evaluators_cross_encoder.md",{"path":665,"priority":436},"references/evaluators_sentence_transformer.md",{"path":667,"priority":436},"references/evaluators_sparse_encoder.md",{"path":528,"priority":436},{"path":670,"priority":436},"references/hf_jobs_execution.md",{"path":672,"priority":436},"references/losses_cross_encoder.md",{"path":674,"priority":436},"references/losses_sentence_transformer.md",{"path":676,"priority":436},"references/losses_sparse_encoder.md",{"path":678,"priority":436},"references/model_architectures.md",{"path":680,"priority":436},"references/prompts_and_instructions.md",{"path":682,"priority":436},"references/training_args.md",{"path":542,"priority":436},{"path":685,"priority":497},"scripts/mine_hard_negatives.py",{"path":687,"priority":497},"scripts/train_cross_encoder_distillation_example.py",{"path":689,"priority":497},"scripts/train_cross_encoder_example.py",{"path":691,"priority":497},"scripts/train_cross_encoder_listwise_example.py",{"path":693,"priority":497},"scripts/train_sentence_transformer_distillation_example.py",{"path":695,"priority":497},"scripts/train_sentence_transformer_example.py",{"path":697,"priority":497},"scripts/train_sentence_transformer_make_multilingual_example.py",{"path":699,"priority":497},"scripts/train_sentence_transformer_matryoshka_example.py",{"path":701,"priority":497},"scripts/train_sentence_transformer_multi_dataset_example.py",{"path":703,"priority":497},"scripts/train_sentence_transformer_static_embedding_example.py",{"path":705,"priority":497},"scripts/train_sentence_transformer_with_lora_example.py",{"path":707,"priority":497},"scripts/train_sparse_encoder_distillation_example.py",{"path":709,"priority":497},"scripts/train_sparse_encoder_example.py",{"basePath":403,"description":711,"displayName":405,"installMethods":712,"rationale":713,"selectedPaths":714,"source":313,"sourceLanguage":18,"type":255},"Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.",{"claudeCode":12},"SKILL.md frontmatter at skills/transformers-js/SKILL.md",[715,716,718,720,722,724,726,728],{"path":323,"priority":307},{"path":717,"priority":436},"references/CACHE.md",{"path":719,"priority":436},"references/CONFIGURATION.md",{"path":721,"priority":436},"references/EXAMPLES.md",{"path":723,"priority":436},"references/MODEL_ARCHITECTURES.md",{"path":725,"priority":436},"references/MODEL_REGISTRY.md",{"path":727,"priority":436},"references/PIPELINE_OPTIONS.md",{"path":729,"priority":436},"references/TEXT_GENERATION.md",{"sources":731},[732],"manual",{"closedIssues90d":240,"description":734,"forks":241,"homepage":735,"license":246,"openIssues90d":242,"pushedAt":243,"readmeSize":238,"stars":244,"topics":736},"Give your agents the power of the Hugging Face ecosystem","https://huggingface.co",[],{"classifiedAt":738,"discoverAt":739,"extractAt":740,"githubAt":740,"updatedAt":738},1778690772996,1778689536128,1778690770714,[214,216,217,213,215],{"evaluatedAt":250,"extractAt":289,"updatedAt":250},[],[745,778,804,823,851,877],{"_creationTime":746,"_id":747,"community":748,"display":749,"identity":755,"providers":759,"relations":769,"tags":773,"workflow":774},1778699289329.1182,"k172jykxz0jywjekjxjt5thj5x86nm3n",{"reviewCount":8},{"description":750,"installMethods":751,"name":753,"sourceUrl":754},"当用户需要通过 Xquik 获取 X (Twitter) 数据或执行需要确认的 X 操作时使用：推文搜索、用户查找、关注者提取、媒体下载、监控、Webhook、MCP、SDK、发布、点赞、私信和个人资料更新。需要 Xquik API 密钥。切勿索要 X 登录凭据。",{"claudeCode":752},"Xquik-dev/x-twitter-scraper","x-twitter-scraper","https://github.com/Xquik-dev/x-twitter-scraper",{"basePath":756,"githubOwner":757,"githubRepo":753,"locale":758,"slug":753,"type":255},"skills/x-twitter-scraper","Xquik-dev","zh-CN",{"evaluate":760,"extract":768},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":762,"targetMarket":218,"tier":219},100,[763,764,214,765,216,766,767],"twitter","x","data-retrieval","mcp","sdk",{"commitSha":280},{"parentExtensionId":770,"repoId":771,"translatedFrom":772},"k17axvhmvwp90strpqcd5b0h7986m80d","kd783enpnwhry153ka0z65ear186mjbh","k172e8vt4zcz50bb0vfp6ptb1n86mf90",[214,216,765,766,767,763,764],{"evaluatedAt":775,"extractAt":776,"updatedAt":777},1778699230863,1778699170774,1778699289329,{"_creationTime":779,"_id":780,"community":781,"display":782,"identity":788,"providers":792,"relations":798,"tags":800,"workflow":801},1778697652123.8982,"k175ckmrqc4x6sjm90k7ejbj3s86ntxs",{"reviewCount":8},{"description":783,"installMethods":784,"name":786,"sourceUrl":787},"Use the Slack tool to react, pin/unpin, send, edit, delete messages, or fetch Slack member info.",{"claudeCode":785},"steipete/clawdis","slack","https://github.com/steipete/clawdis",{"basePath":789,"githubOwner":790,"githubRepo":791,"locale":18,"slug":786,"type":255},"skills/slack","steipete","clawdis",{"evaluate":793,"extract":797},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":794,"targetMarket":218,"tier":219},[786,795,796,216,214],"messaging","communication",{"commitSha":280},{"repoId":799},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[214,216,796,795,786],{"evaluatedAt":802,"extractAt":803,"updatedAt":802},1778698950505,1778697652123,{"_creationTime":805,"_id":806,"community":807,"display":808,"identity":812,"providers":814,"relations":819,"tags":820,"workflow":821},1778697652123.8928,"k171pew5empzzrfghyg9nqrk6n86nqa9",{"reviewCount":8},{"description":809,"installMethods":810,"name":811,"sourceUrl":787},"Use gh for GitHub issues, PR status, CI/logs, comments, reviews, releases, and API queries.",{"claudeCode":785},"github",{"basePath":813,"githubOwner":790,"githubRepo":791,"locale":18,"slug":811,"type":255},"skills/github",{"evaluate":815,"extract":818},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":816,"targetMarket":218,"tier":219},[811,277,214,817,216],"developer-tools",{"commitSha":280},{"repoId":799},[214,216,277,817,811],{"evaluatedAt":822,"extractAt":803,"updatedAt":822},1778698569289,{"_creationTime":824,"_id":825,"community":826,"display":827,"identity":833,"providers":837,"relations":845,"tags":847,"workflow":848},1778696993586.708,"k17fsfrfvbnsvwkcqp8y85wdad86mmwq",{"reviewCount":8},{"description":828,"installMethods":829,"name":831,"sourceUrl":832},"Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.",{"claudeCode":830},"SeifBenayed/claude-code-sdk","product-self-knowledge","https://github.com/SeifBenayed/claude-code-sdk",{"basePath":834,"githubOwner":835,"githubRepo":836,"locale":18,"slug":831,"type":255},".claude/skills/product-self-knowledge","SeifBenayed","claude-code-sdk",{"evaluate":838,"extract":844},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":839,"targetMarket":218,"tier":219},[840,841,842,214,767,843],"anthropic","documentation","claude","knowledge-base",{"commitSha":280},{"repoId":846},"kd78s53c1852h5p7c3qem663xs86njab",[840,214,842,841,843,767],{"evaluatedAt":849,"extractAt":850,"updatedAt":849},1778697182451,1778696993586,{"_creationTime":852,"_id":853,"community":854,"display":855,"identity":861,"providers":865,"relations":870,"tags":873,"workflow":874},1778696833339.6226,"k17ckxne6mhyf23n1jfyqktpqd86nfz4",{"reviewCount":8},{"description":856,"installMethods":857,"name":859,"sourceUrl":860},"Interact with Google Docs - create documents, search by title, read content, and edit text.\nUse when user asks to: create a Google Doc, find a document, read doc content, add text to a doc,\nor replace text in a document. Lightweight alternative to full Google Workspace MCP server with\nstandalone OAuth authentication.\n",{"claudeCode":858},"sanjay3290/ai-skills","google-docs","https://github.com/sanjay3290/ai-skills",{"basePath":862,"githubOwner":863,"githubRepo":864,"locale":18,"slug":859,"type":255},"skills/google-docs","sanjay3290","ai-skills",{"evaluate":866,"extract":869},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":867,"targetMarket":218,"tier":219},[859,214,841,868,278],"oauth",{"commitSha":280},{"parentExtensionId":871,"repoId":872},"k17es37z10n1sw6t2m3f0vsydx86mnje","kd71np0fyqg23qg8w2hcfw0h0h86nkn0",[214,841,859,868,278],{"evaluatedAt":875,"extractAt":876,"updatedAt":875},1778696994497,1778696833339,{"_creationTime":878,"_id":879,"community":880,"display":881,"identity":887,"providers":893,"relations":899,"tags":902,"workflow":903},1778696505500.0078,"k174n9sd7wv9knh3b8rv7vv2wh86me74",{"reviewCount":8},{"description":882,"installMethods":883,"name":885,"sourceUrl":886},"Search and retrieve content from Reddit. Get posts, comments, subreddit info, and user profiles via the public JSON API. Use when user mentions Reddit, a subreddit, or r/ links.",{"claudeCode":884},"ReScienceLab/opc-skills","Reddit","https://github.com/ReScienceLab/opc-skills",{"basePath":888,"githubOwner":889,"githubRepo":890,"locale":891,"slug":892,"type":255},"skills/reddit","ReScienceLab","opc-skills","fr","reddit",{"evaluate":894,"extract":898},{"promptVersionExtension":206,"promptVersionScoring":207,"score":761,"tags":895,"targetMarket":218,"tier":219},[892,214,765,896,897],"social-media","information-gathering",{"commitSha":280,"license":246},{"parentExtensionId":900,"repoId":901},"k17b55rp7ccqw91566yq0ax2as86n6rk","kd7fj56h5kejcgm6hcjmzn79xd86m7wa",[214,765,897,892,896],{"evaluatedAt":904,"extractAt":905,"updatedAt":904},1778696852717,1778696505500]