Gemini AI Agent Skills

English | 简体中文 🧠 The data layer for AI systems. Skill Seekers turns any documentation, GitHub repo, or PDF into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours. Skill Seekers is the universal preprocessing layer that sits between raw documentatio

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on 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, and model persistence.

When the user wants to create or update a README.md file for a project. Also use when the user says "write readme," "create readme," "document this project," "project documentation," or asks for help with README.md. This skill creates absurdly thorough documentation covering local setup, architecture, and deployment.

Use this skill when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

prompts.chat The world's largest open-source prompt library for AI Works with ChatGPT, Claude, Gemini, Llama, Mistral, and more formerly known as Awesome ChatGPT Prompts 🌐 Browse Prompts • 📖 Read the Book • 📄 View on GitHub • 🚀 Self-Host 🏆 Featured in Forbes · 🎓 Referenced by Harvard , Columbia · 📄 40+ academic citations · ❤️ Most liked dataset on Hugging Face ⭐ 143k+ GitHub stars ·

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.

View All 21 Categories - Our Mission - Path Towards AI Research Agent - Available AI Research Engineering Skills - Skill Structure - Repository Structure - Use Cases - Contributing - Community We provide the layer of Engineering Ability that enable your coding agent to write and conduct AI research experiments, including preparing datasets, executing training pipelines, deploying models, and build

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 model evaluations with vLLM/lighteval. Works with the model-index metadata format.

+W is a universal "save for later" action for commerce. This MCP server lets AI assistants save any product URL to a user's Wishfinity wishlist with one click. Works with Claude, ChatGPT, Gemini, LangChain, OpenAI Agents SDK, and any MCP-compatible client. When an AI recommends a product, it can offer +W Add to Wishlist. The user clicks the link, and the product is saved to their Wishfinity accoun

Automate GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, setting up PR review automation, creating GitHub Actions, or triaging issues.

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks.

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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.

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.

Multi-agent orchestration platform for enterprise AI development. Run 12+ AI agents concurrently with full governance, Gemini-powered code review, and enforced quality gates. Solves coordination, verification, and metrics challenges that stall enterprise AI adoption.

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

A local Model Context Protocol (MCP) server that provides structured, high-signal insight into a Git repository, enabling AI coding agents to understand project state, structure, activity, and risk — without uploading or modifying the codebase. AI coding agents (Claude Code, Cursor, Codex, Gemini, etc.) are powerful, but they lack situational awareness when working with non-trivial codebases. They

Generate marketing screenshots of your app using Playwright. Use when the user wants to create screenshots for Product Hunt, social media, landing pages, or documentation.

ROS-MCP-Server connects large language models (such as Claude, GPT, and Gemini) with existing robots giving them bidirectional AI integration. With no changes to existing robot source code, this enables: - 🗣 Commanding the robot in natural language → instructions are translated into ROS/ROS2 commands. - 👀 Giving AI full visibility → subscribe to topics, call services, read sensor data, and monit

Stop copy-pasting between AI models. Roundtable AI is a local MCP server that lets your primary AI assistant delegate tasks to specialized models like Gemini, Claude, Codex, and Cursor. Solve complex engineering problems in parallel, directly from your IDE. Key Features: - Context Continuity: Shared project context across all sub-agents - Parallel Execution: All agents work simultaneously - Model

Open-source Agent Skills for computational materials science and numerical simulation workflows. Give your AI coding agent domain expertise in numerical methods, simulation best practices, and scientific computing -- without re-explaining the same concepts every session. Skills are portable across Claude Code, Codex, Gemini CLI, Cursor, VS Code Copilot, and 20+ other compatible tools. - The Proble

Give any AI the ability to build, validate, and deploy production multi-agent systems. This is an MCP server that connects AI assistants — ChatGPT, Claude, Gemini, Copilot, Cursor, Windsurf, and any MCP-compatible environment — directly to the ADAS platform. An AI developer says "Build me a customer support system with order tracking and escalation" — and their AI assistant handles the entire life

Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.