Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
supabase-sync
by 3balljugglerYuVerify and synchronize Supabase resources (migrations + Edge Functions) between the local repository and the linked remote project for `ai_coordinate`. Use this skill when the user wants to apply pending migrations, deploy an Edge Function, check drift between local and remote state, or confirm DB / Function readiness before/after a release. Triggers include "マイグレーション適用", "マイグレーション同期", "リモートと一致してる？", "drift チェック", "db push", "supabase migration apply", "Edge Function デプロイ", "supabase functions deploy", "image-gen-worker デプロイ", "supabase 周り整えて", "次の開発に進める状態か確認して".
spec-verifying
by 3balljugglerYuVerifies consistency between EARS specifications and implemented tests. Use when checking test coverage, validating spec-test alignment, generating coverage reports, or auditing test completeness.
spec-writing
by 3balljugglerYuClarifies and refines EARS specifications through interactive dialogue. Use when reviewing specs, adding missing conditions, validating requirements, or improving specification quality.
supabase-postgres-best-practices
by 3balljugglerYuPostgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.
test-checklisting
by 3balljugglerYuManages Next.js test implementation progress tracking. Use when checking progress, finding next test targets, or reviewing completion status by Tier priority.
test-fixing
by 3balljugglerYuGuides proper test failure resolution based on specifications. Prevents anti-patterns like modifying tests just to pass. Use when tests fail, fixing test errors, or debugging test issues.
test-generating
by 3balljugglerYuGenerates test code from EARS specifications following London School (mockist) approach. Use when creating tests from specs, implementing test cases, or generating test scaffolds.
test-reviewing
by 3balljugglerYuReviews generated tests by running them, analyzing failures, and validating London School patterns. Use when reviewing tests, checking test quality, or before spec verification.
testing-flow
by 3balljugglerYuOrchestrates the complete Next.js test implementation workflow. Use to guide through test creation steps, check dependencies, and determine the next action for any target.
architecture-documentation
by 3balljugglerYuGenerate architecture documents using templates with diagram integration. Use for creating C4 diagrams, viewpoint documents, and technical overviews.
characterization-testing
by 3balljugglerYuCreates characterization tests to capture existing behavior before refactoring. Use when writing char tests, creating golden tests, preparing for refactoring, or preserving legacy code behavior.
codex-webpack-build
by 3balljugglerYuRepository-specific production build verification workflow for this Next.js app. Use when a user asks Codex to run a build, verify whether a change builds, investigate build failures, or confirm release readiness in this repo. In Codex and sandbox environments, prefer `npm run build -- --webpack` over plain `npm run build` because the default Turbopack build can stall here while webpack completes. If the user explicitly asks to test Turbopack or change build tooling, follow that request instead.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.