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...
oa-quality-gates
by OpenAEC-FoundationMandatory 5-check quality gate after every agent batch. Use when Claude needs to validate batch output before proceeding to the next phase. Activates for: batch complete, agent done, oa collect, validate output, quality check, all agents finished.
oa-quality-guardians
by OpenAEC-FoundationAutomatically update core documentation after agent work completes. Use when Claude needs to spawn guardian agents to update LESSONS.md, ROADMAP.md, or DECISIONS.md after a batch or session. Activates for: batch done, session end, guardian, update docs, lessons learned.
oa-skill-watcher
by OpenAEC-FoundationDetects newly created or recently modified skills in ~/.claude/skills/ and auto-generates matching agent templates in the Open-Agents library. Use when a new skill has been created, when skills have been updated, or when the user says 'generate template for this skill', 'scan new skills', 'update agent library from skills', or 'watch skill changes'.
oa-state-agents-json
by OpenAEC-FoundationDeterministic reference for the Open-Agents state file (agents.json). Use when Claude needs to inspect agent state, read AgentRecord fields, understand status values, or trace state transitions. Activates for: agents.json, agent state, status running/done/failed, AgentRecord fields, ~/.oa/, state file.
oa-state-checkpoint
by OpenAEC-FoundationCLI reference for checkpoint and resume — crash recovery for long-running agents. Use when an agent crashes mid-task or you need to save progress for later resumption. Activates for: oa checkpoint, oa resume, crash recovery, checkpoint agent.
oa-state-collect
by OpenAEC-FoundationDeterministic reference for collecting agent output in Open-Agents. Use when Claude needs to retrieve completed agent results, understand output file locations, or choose between collect/watch/attach commands. Activates for: oa collect, output/result.md, agent output, oa watch, oa attach, read agent results.
oa-state-lifecycle
by OpenAEC-FoundationCLI reference for managing running agents: stopping, cleaning, attaching, watching, listing. Use when checking agent state, stopping agents, or cleaning workspaces. Activates for: oa kill, oa clean, oa attach, oa watch, oa status, agent lifecycle.
oa-state-workspace
by OpenAEC-FoundationExplains oa agent workspace layout, --direct flag, and how agents write output. Use when understanding where agent files go or how to access agent output. Activates for: workspace, agent output, --direct, /tmp/oa-agent, result.md, project_root.
oa-teams-coordination
by OpenAEC-FoundationCoordinate multi-agent teams using staging patterns and shared result directories. Use when managing a group of workers writing to shared output, sequencing phases, or using oa team commands. Activates for: oa team, staging area, shared results, phase coordination, L-005.
oa-web-dashboard
by OpenAEC-FoundationAccess and interpret the oa web UI and Textual TUI. Use when opening the dashboard, checking agent status visually, reading logs, or using the bridge API. Activates for: oa dashboard, oa web, web UI, localhost:5174, bridge API.
brand-guidelines
by OpenAEC-FoundationApplies OpenAEC Foundation brand to artifacts. WORKSPACE OVERRIDE — overrides global Impertio brand. Triggers on: branding, huisstijl, corporate identity, visual design, OpenAEC style.
oa-agent-library-builder
by OpenAEC-FoundationGrows the agent library from successful oa runs. Use when reviewing completed agent output that follows a clear, repeatable pattern, or when the user mentions saving an agent as a template. Activates for: oa collect shows success, save as template, make reusable, add to library.
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.