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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evaluate-issue
by shakacodeUse before fixing, batching, or assigning GitHub issues or proposed fixes when the value is uncertain, the report came from AI/code analysis, the fix is complex, or the user asks whether an issue is worth doing. Produces an evidence-backed recommendation: fix now, document/work around, park, close, or ask for product input.
update-changelog
by shakacodeAnalyze merged PRs and update CHANGELOG.md, optionally stamping release, rc, beta, or explicit version headers. Use before releases or when changelog entries are missing.
verify
by shakacodeRun a local verification loop for the current branch before creating or updating a PR, selecting checks from AGENTS.md and changed files. Use when asked to verify, test, or prepare PR changes.
discover-abtests
by shakacodeCrawl a website and auto-generate .abtest.ts files for shaka-perf visreg visual regression testing. Use this skill whenever the user wants to discover, generate, or scaffold AB tests for a URL — even if they just say "set up tests for localhost:3020", "generate tests for this site", or "create visreg tests".
setup-docker-servers-for-ab-tests
by shakacodeSet up shaka-perf twin-servers — the Docker A/B testing infrastructure that runs your app twice (control vs experiment) so visreg/perf can compare two branches. Use this skill whenever the user wants to set up, configure, or debug twin-servers, "dockerize" their app for shaka-perf, write the twin-servers Dockerfile/Procfile/docker-compose, fill in the `twinServers` config, or get `shaka-perf servers` building and running — even if they just say "set up twin servers", "get the A/B servers running", or "make my app run under shaka-perf for perf testing".
address-review
by shakacodeFetch GitHub PR review comments, triage them into must-fix/discuss/optional/skipped, and guide fixing or replying to selected feedback. Use when addressing PR review comments or review threads.
adversarial-pr-review
by shakacodeUse when a PR needs skeptical pre-merge or post-merge risk review, especially after concurrent agent work, before merge readiness, before a release candidate, or when Codex or Claude should red-team correctness, security, compatibility, changelog, validation, and review-gate risks.
autoreview
by shakacodeRun a structured second-model code review as a closeout gate on a local, branch, or commit diff, then verify every finding against the real code and loop until clean. Use before commit/push/ship in this repo.
plan-pr-batch
by shakacodeUse when choosing GitHub issues or PRs for a PR batch, preparing a subagent batch plan, or producing a ready goal prompt that invokes pr-batch.
post-merge-audit
by shakacodeUse when auditing merged PRs after concurrent agent work, before a release candidate, after a suspected bad merge, or when checking for missed reviews, missing changelog entries, cross-PR interactions, or release risk.
pr-batch
by shakacodePlan and safely launch batches of issue or PR work, especially when using Codex subagents, multiple worktrees, or multiple machines. Use when the user asks to run a Codex batch, process several issues or PRs, split work across agents or machines, or turn filters into a PR-processing plan and /goal prompt.
run-ci
by shakacodeAnalyze current branch changes with the repo CI detector and run user-selected local CI jobs. Use when the user asks to run, reproduce, or choose local CI checks.
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.