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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04-shadow-areas
by ai-driven-devAnalytical scan of a markdown artifact (idea, user-stories, PRD, spec) to surface blind spots - unstated assumption, missing actor, missing failure mode, ambiguous term, missing acceptance criterion, missing edge case, and missing dependency - emitting a structured shadow report grouped by category and sorted by severity. Use when the user says "find blind spots in this spec", "what's missing in this PRD", "shadow report", "shadow analysis", "scan for gaps", "find what's missing", "spot blind spots", "review for gaps", or asks for an analytical gap scan of a written artifact. Do NOT use for interactive clarification through iterative Q&A (use aidd-refine:01-brainstorm for that), implementing features, writing tests, or reviewing code style.
09-for-sure
by ai-driven-devIterative agent loop that tracks attempts and retries until a success condition is met. Use when the user says "for sure", "make sure", "keep trying until", "loop until done", "don't stop until", or needs guaranteed completion of a task with explicit success criteria.
04-spec
by ai-driven-devGenerate or refine a project spec from a free-form human request, an existing PRD, or reviewer findings. Use when the user says "draft spec", "spec for X", "refine the spec", "generate spec from prd", "/spec", or when an orchestrator needs a normalized contract before planning. Do NOT use for writing source code, drafting a full PRD, or modifying a validated and locked spec.
04-issue-create
by ai-driven-devCreate an issue in the configured ticketing tool. Use when the user says "new issue", "create an issue", "file a bug", "file an issue", "report bug", "open an issue", or invokes `/issue-create`. Do NOT use for committing changes, opening pull requests, tagging releases, or commenting on existing issues.
04-audit
by ai-driven-devRead-only codebase audit across quality pillars (code-quality, architecture, security, dependencies, performance, tests, ui). Diagnoses and reports findings; never edits code. Use when the user wants to assess, audit, or health-check a codebase or one dimension of it, then hands off to the act-skills (refactor, test, impeccable) to fix. Do NOT use for fixing the findings (hand off to refactor/test/impeccable), per-PR code review (use 05-review), or validating that a feature works (use 03-assert).
04-mermaid
by ai-driven-devGenerate high-quality Mermaid diagrams from markdown content using a structured plan-validate workflow.
05-learn
by ai-driven-devCapture and store project-level learnings, conventions, and decisions surfaced during work into memory, decisions, or rules. Use proactively when the user states a durable project rule or convention ("for next", "always do X", "from now on", "going forward", "rule:", "convention:"), records a technical decision and its rationale, deprecates something, or notes an insight that should outlive the current task. Do NOT use for personal or AI-preference reminders (those belong to user memory), routine code edits, minor fixes, or anything already captured.
05-review
by ai-driven-devRead-only review of a diff (a PR or working changes) - code quality against project rules, and feature behavior against the plan's acceptance criteria. Surfaces findings with a verdict; never patches. Use to review changes in progress. Do NOT use for a whole-codebase health check (use 04-audit), fixing the findings (hand off to 07-refactor / 02-implement / 08-debug), or validating a feature runs (use 03-assert).
05-fact-check
by ai-driven-devVerify factual claims in a piece of text against authoritative sources and rewrite it with footnote citations, hedging any claim that cannot be confirmed. Runs a cheapest-first verification cascade (project memory and docs, then codebase inspection, then web lookup) and reports both sources when they disagree. Use when the user says "fact-check this", "verify that claim", "are you sure about that", "is that actually true", "cite your sources", "where did you get that fact", "did you make that up", "double-check the version you gave me", "vérifie cette information", or "es-tu sûr de ça". Do NOT use to auto-guard the AI's own output (this skill only fires on an explicit request), to judge code logic correctness, or to clarify vague requirements through iterative Q&A - use `aidd-refine:01-brainstorm` for that.
06-discovery
by ai-driven-devEnumerate installed surfaces of the AI tool (skills, agents, commands, plugins, MCP servers, rules, hooks, memory files) and recommend the best match for the user's stated intent. Use proactively whenever the user asks the model to list, show, enumerate, find, or pick among any of these surfaces - including imperative phrasings ("list hooks", "show me the rules", "enumerate skills", "find a memory file", "which agent reviews code"), question phrasings ("what's available?", "what hooks do we have?", "which rule applies here?", "what memory files do we have?"), and indirect phrasings ("what can I use for X?", "do we have something that does Y?"). Always pick this skill over scanning the filesystem with grep, find, ls, or reading action files directly when the user is enumerating a surface. Do NOT use for picking a specific item inside one plugin (the plugin's own onboard handles that), creating a new surface, or executing a recommended item (this skill only points; the user invokes).
06-test
by ai-driven-devWrite and iterate on tests until they pass, and validate user journeys end-to-end in the browser.
08-debug
by ai-driven-devReproduce and fix bugs systematically using test-driven workflow, root cause analysis, and hypothesis validation.
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.