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.
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cw-spec
by sighupGenerates a structured specification with demoable units, functional requirements, and proof artifact definitions. This skill should be used when starting a new feature to define what will be built before any code is written.
cw-dispatch-team
by sighupPersistent agent team dispatcher with lead coordination. This skill should be used after cw-plan to execute tasks via a managed team (requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 and CLAUDE_CODE_TASK_LIST_ID).
cw-testing
by sighupE2E testing with auto-fix. Generates tests from specs, executes in isolated sub-agents, and auto-fixes application bugs. This skill should be used after implementation to verify end-to-end behavior.
cw-plan
by sighupTransforms a specification into a task graph with dependencies. This skill should be used after cw-spec to break a spec into executable tasks with proper sequencing before dispatching with cw-dispatch.
cw-research
by sighupPerforms preliminary codebase fact-finding and produces a structured research report. This skill should be used before cw-spec to understand an unfamiliar or complex codebase and generate enriched context for specification writing.
cw-gherkin
by sighupInternal subagent that generates Gherkin BDD scenarios from spec acceptance criteria. Produces one .feature file per demoable unit in the spec directory and optionally creates cw-testing task stubs on the task board. Called automatically by cw-spec.
cw-worktree
by sighupManages git worktrees for parallel feature development. This skill should be used when starting multiple features at once, or to list, switch between, and merge existing worktrees.
cw-validate
by sighupValidates implementation against spec using 6 gates and generates a coverage matrix. This skill should be used after implementation is complete to verify coverage, proof artifacts, and credential safety before review.
cw-dispatch
by sighupIdentifies independent tasks and spawns parallel agent workers. This skill should be used after cw-plan to execute multiple tasks concurrently.
cw-review-team
by sighupTeam-based concern-partitioned code review. Each reviewer sees ALL files through a specialized lens (security, correctness, spec compliance). This skill should be used after cw-validate for thorough cross-file review (requires CLAUDE_CODE_TASK_LIST_ID).
cw-execute
by sighupExecutes a single task from the task board using the 11-step implementation protocol. This skill should be used after cw-plan or cw-dispatch assigns a task, or when manually implementing a specific task by ID.
cw-review
by sighupReviews implementation code for bugs, security issues, and quality problems. Creates FIX tasks for issues found. This skill should be used after cw-validate to catch issues before merge.
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.