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...
maestro-dev
by ReinaMacCredyDevelopment workflow for maestroCLI itself. Encodes the hexagonal architecture pattern (port -> adapter -> use-case -> command -> MCP tool -> test) and project-specific conventions. Use when implementing new maestro features, adding CLI commands, extending the MCP server, creating new adapters, modifying ports, writing use-cases, or debugging maestro's own code. Also use when you need to understand how maestro's layers connect or where to put new code.
docs
by ReinaMacCredyUpdate repository documentation to match the current state of the codebase. Local replacement for the remote /docs command (which needs the Claude GitHub app). Use when the user says /docs, "update the docs", "sync the README", "document this feature", or asks you to refresh docs to reflect current code. Works inline in the current session and edits files in the working tree.
maestro-agent-base
by ReinaMacCredyBase procedures for all mission agents: startup, cleanup, and handoff. REQUIRED skill for all mission feature implementations.
maestro-blueprint
by ReinaMacCredyGenerate visual HTML blueprint pages and structured plan specs for maestro project features. Explores the codebase, produces a `.md` plan in maestro format (Context, Critical Files, Design Decisions, Phases with Tasks and acceptance criteria) saved to `.maestro/plans/`, plus a visual HTML presentation. Use when the user asks to blueprint a maestro feature, plan an implementation for this project, or says "blueprint X" while working in the maestro codebase. Also use proactively for non-trivial maestro changes that span multiple files or architectural concerns.
maestro-conduct
by ReinaMacCredyEnter conductor mode: plan, decompose, and dispatch -- sub-agents implement, not you. Use when user says 'you orchestrate', 'conduct this', 'delegate this', 'don't code yourself', 'break this into sub-agents', 'run this milestone without doing it yourself', or wants to stay in the driver seat while you manage agents. Works for formal mission/milestone execution and ad-hoc decomposition.
maestro-define-mission-skills
by ReinaMacCredyDefine and register custom skills for use in Mission Control missions. Create skill definitions with frontmatter, procedures, and validation rules.
maestro-mission-planning
by ReinaMacCredyPlan and structure new missions. Brainstorm raw ideas into decomposed missions with milestones, features, agent types, constraints, and the exact `maestro handoff` command for the first external agent.
maestro-scrutiny-validator
by ReinaMacCredyRun code scrutiny validation during mission checkpoints. Spawns review subagents, synthesizes results, and produces validation reports.
maestro-user-testing-validator
by ReinaMacCredyRun user testing validation during mission checkpoints. Determines testable assertions, sets up test environment, spawns flow validators, and synthesizes results.
maestro-lifecycle-test
by ReinaMacCredyValidate a built maestro binary end to end (install/init completeness + the feature->task->QA lifecycle + standalone tasks + real-use edge cases + agent-UX) in an isolated throwaway repo, emitting a machine-aggregatable NDJSON report. Run as one sub-agent of a swarm fix-loop, or standalone to smoke-test a change.
maestro-skill-author
by ReinaMacCredyCreate, update, or debug maestro built-in skills. Covers SKILL.md frontmatter, reference directory structure, step-file architecture, build-time embedding, naming conventions, alias management, and registry validation. Use when creating a new maestro built-in skill, modifying an existing SKILL.md, adding reference files, debugging skill loading failures, updating the skills registry, or working on the skills full port. Also use when frontmatter validation fails, skills don't appear in skill-list, or reference files fail to load.
maestro-audit
by ReinaMacCredyUse for read-only Maestro repo audits that propose harness backlog improvements without implementing them.
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.