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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researcher
by krzysztofdudekUse when user wants to optimize or tune something measurable through repeated experiments — "make X faster", "improve [metric]", "find best config", "iterate overnight", "run experiments until it hits N". Triggers on quantitative goals (build time, latency, pass rate, accuracy) or qualitative ones scoreable against a rubric (prompt quality, doc parsing). Skip for one-shot bugs with a clear fix or tasks where the user wants direct implementation without exploration.
context
by krzysztofdudekUse when the agent has a concrete file path (about to edit `src/api/users.ts`) and needs the rules that apply to THAT path. Also use with no path to list all rule ids — triggers "what rules does this project have", "list all rules", "which rules exist", "show me the rules". Other triggers: "before I edit X, what rules apply?", a path reference without a review request. Skip when the agent wants an actual verdict (use autoreview:review); skip when no path yet and the user asks an abstract convention question (use autoreview:guide); skip when no `.autoreview/` exists — use autoreview:setup first.
create-rule
by krzysztofdudekUse when user wants a new code convention rule added, regardless of phrasing — "add a rule", "enforce X", "forbid Y", "write a rule for Z", "create `.autoreview/rules/foo.md` with body ...". Once `.autoreview/` exists, invoke this skill for rule authoring even when the user asks to skip the wizard or specifies the exact file path and body — the wizard guards rule quality. Triggered whenever the user describes a pattern they want checked on every commit. Skip when no `.autoreview/` exists — use autoreview:setup first, then come back here.
guide
by krzysztofdudekUse when the user asks a free-text question about a convention without a concrete file path ("how do I write a command handler here?", "what's the logging policy?"). The agent needs knowledge retrieval, not a review verdict. Skip when a file path is already known (use autoreview:context); skip when the user actually wants a pass/fail verdict on existing code (use autoreview:review); skip when no `.autoreview/` exists — use autoreview:setup first.
history
by krzysztofdudekUse when the user asks about review verdicts over time — "what's been getting rejected", "rejection rate this week", "show recent reviews", "history of verdicts", "which rule fails most often", "show suppressed reviews", or filtering reviews by rule / file / date / verdict / tier / severity. Zero LLM cost — pure read of stored review records. Skip when the user wants a FRESH verdict (use autoreview:review) or when no `.autoreview/` exists (use autoreview:setup first).
override-rule
by krzysztofdudekUse when user wants to adapt a remote rule's tier / triggers / severity / type WITHOUT forking the rule. Wizard for editing config.yaml's `remote_rules[].overrides` block. Triggers include "this corp rule is too noisy in my repo", "downgrade this rule to warning", "narrow this rule to only src/", "make this rule manual-only". Skip when the rule is local — local rules are owned by the user; edit the file directly. Skip when user wants to change rule body — that's a fork, not an overlay.
pull-remote
by krzysztofdudekUse when the user wants to fetch or refresh remote rules — "pull rules from upstream", "update remote rules", "fetch latest rule version", "sync remote rules" — or right after editing `remote_rules` in `.autoreview/config.yaml`. Also use when a review fails because a referenced remote rule isn't yet on disk. Skip when no `remote_rules` are declared in config (the tool will warn and exit cleanly anyway).
review
by krzysztofdudekUse when user asks "does this pass review?", "does this file pass our rules", wrapping up feature work before a commit, when a commit got blocked OR rejected by the pre-commit hook (any "[reject]", "commit rejected", "commit being rejected", "why is my commit being rejected", "why is my commit failing", "my commit is being blocked" phrasing), or when debugging a specific rule verdict. Runs the real LLM reviewer. Skip when no `.autoreview/` exists — use autoreview:setup.
setup
by krzysztofdudekUse when the user asks to set up AutoReview in the repo ("setup autoreview", "init autoreview", "initialize autoreview", "bootstrap autoreview", "add autoreview here", "install autoreview") or when `.autoreview/` is missing and the user wants code review. Also triggers when the agent notices no config but rules are being discussed. Skip when `.autoreview/` already exists and the user is not asking for a reset (use autoreview:create-rule instead to add rules).
ratatoskr
by krzysztofdudekUse on every user-facing turn, always — load it before you say anything to any user about this codebase or product. It is a standing discipline for how you communicate, not a tool tied to a particular activity; there is no turn on which you skip loading it. It governs change requests (feature, fix, refactor touching user-visible behavior, account management, data export or deletion, billing, payments, notifications, auth, retention, sharing, compliance like GDPR / PCI / HIPAA) and equally pure questions, explanations, code walkthroughs, and technical/user-visible translations. Plain-language discipline, the no-ceremony / no-machinery-names rule, no time or effort estimates, and question discipline apply on every turn without exception. Load it even when a written spec says implement verbatim, even for a pure internal rename, and even for an already-locked fix — loading is free; the skill itself decides how light to keep the turn, escalating to confirmation gates only when an operation is destructive, irrevers
precheck
by krzysztofdudekUse when user has drafted file content that is NOT on disk yet and wants a reviewer verdict before writing — explicit phrases like "would this pass review?", "check before I save", "will this be accepted?" with content not yet persisted. Costs 1 LLM call per rule. If the user references an existing file ("does this file pass", "check src/foo.ts") use autoreview:review instead — precheck requires unsaved draft content (pasted, or pointed at via `--content-file`). For plain "I'm about to edit file X", use autoreview:context (free) — don't precheck every edit. Skip when no `.autoreview/` exists — use autoreview:setup first.
urd
by krzysztofdudekUse when transitioning from a plan, design doc, or spec into implementation - optimize for accuracy over speed and cost; ask the user when confused, when the spec is ambiguous, or when tempted by a hack rather than guessing or working around it
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.