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...
tdd
by 8thlightBoundary-focused TDD workflow enforcing L3/L4 altitude testing, property-based tests, and red-green-refactor phase separation. Use when starting a new module or feature with test-first discipline.
implement
by 8thlightExecute an approved implementation plan using task-graph-driven orchestration with isolated TDD agents (RED/GREEN/VALIDATE gates). Reads a task graph from yaks, beads, or native tasks and dispatches agents phase by phase. Use when the user has an approved plan and wants to start building, execute a task graph, resume an interrupted implementation, or pick up where a previous session left off. Do NOT use for planning (use plan-tasks instead) or codebase exploration (use research instead). Triggers on phrases like "implement the plan", "execute the plan", "start building", "resume implementation", "pick up where we left off", "dispatch agents", or "build from plan".
plan-tasks
by 8thlightCreate a detailed implementation plan with test specs, phased task breakdown, and Agent Context blocks for agent-driven execution. Consumes a research artifact or works from a feature description directly. Creates a task graph in yaks, beads, or native tasks. Use when the user wants to plan a feature, create an implementation roadmap, break down work into tasks, or prepare for TDD implementation. Do NOT use for codebase exploration (use research instead) or executing a plan (use implement instead). Triggers on phrases like "plan the implementation", "create a roadmap", "break this into tasks", "plan before coding", "draft a plan", "turn research into a plan", or "create task breakdown".
harness
by 8thlightAssess and strengthen the agentic coding environment surrounding a codebase. Audits instruction files, hooks, type safety, linting, pre-commit, architecture tests, CI gates, and sandbox permissions — then produces a gap analysis with actionable improvements. Use when the user wants to improve their Claude Code setup, harden their development environment for AI agents, add hooks or guardrails, assess harness maturity, or mentions "harness engineering". Also use when the user says things like "make my repo more agent-friendly", "add guardrails", "improve my Claude setup", "audit my dev environment", or "what controls am I missing".
reflect
by 8thlightPost-session skill to reflect on what was built and produce improvement proposals. Reads recent git history, artifacts, and context files to extract learnings for skills, CLAUDE.md, hooks, and plan templates. Use after any substantive session as a post-mortem or retrospective.
adr
by 8thlightGuides writing minimal Architecture Decision Records (ADRs). Use when recording architectural decisions, documenting design choices, capturing technical decisions with context and alternatives, or when user mentions ADR, architecture decision, or decision record.
research
by 8thlightExplore a codebase and research external patterns before building a non-trivial feature. Spawns parallel subagents for codebase exploration AND web/pattern research, then synthesizes findings into a compact research artifact at .light/sessions/. Use when the user wants to understand existing code, investigate architecture, find best practices, or scope out a feature before planning or implementing. Do NOT use for debugging failures, fixing bugs, or post-implementation reflection. Triggers on phrases like "research the codebase", "explore before building", "understand how X works", "what exists for Y", "help me scope this feature", "deep dive into", or "before I start building".
prepare-cop-notes
by 8thlightUse when a CoP scribe has exported the Notes tab from Google Docs as markdown and needs to add frontmatter and save it to the sessions/ directory.
scribe-retrospective
by 8thlightUse after committing a CoP session transcript. Asks if you fixed anything manually after Claude's review and updates skill tables if needed.
scribe-workflow
by 8thlightUse when a CoP scribe needs to process a Community of Practice meeting transcript after a session. Orchestrates the full workflow from cleaning the transcript through PR creation, delegating to specialized sub-skills.
using-git
by 8thlightUse when working in the a11y-cop repository and need to perform git or GitHub operations. This is THE authoritative guidance for proper git/gh interaction in this repo, including branching, commits, and PR creation conventions.
workflow-feedback
by 8thlightUse when you need to capture subjective user feedback on what didn't work well in the scribe workflow (documentation, skills, or process). Focuses only on improvements, offers options for how to handle the feedback (add to PR, create issue, or contact facilitator).
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.