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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scaffold
by mikeparcewskiComponent scaffolding toolkit for creating skills, agents, commands, and hooks within the unified wicked-garden plugin. Generates complete, valid structures that pass validation out-of-the-box. Use when creating new domain components, setting up boilerplate, or ensuring proper structure from the start.
unit-test-quality
by mikeparcewskiLanguage- and framework-neutral guard against useless unit tests — the kind that pass forever, never catch regressions, and inflate coverage without protecting behavior. Provides two complementary thought-experiment filters (execution + behavior), a pre-write self-check, a taxonomy of seven recurring anti-patterns (tautological, assertion-free, implementation mirror, framework retest, constant verification, sleep-coupled, exception-swallowing), and a one-line decision rule per pattern. Use when: writing or reviewing a unit test, auditing a suite with high coverage but low confidence, or pairing a regression test with a bug fix.
engineering
by mikeparcewskiSenior engineering guidance on implementation, code quality, and maintainability. Use when reviewing code for R1-R6 standards violations (dead code, bare panics, magic values, swallowed errors, unbounded ops, god functions) or getting cross-cutting implementation advice. NOT for architecture decisions (use the architecture skill) or debugging (use the debugging skill).
deliberate
by mikeparcewskiCritical thinking framework applied before doing work. Challenges assumptions, reframes problems, identifies hidden opportunities, and validates whether the stated ask is the right ask. A way of approaching work, not a specialist discipline. Use when: challenging assumptions before implementation, reframing a stated problem, or asking whether the right thing is being asked at all.
codebase-narrator
by mikeparcewskiNarrate codebase structure and architecture for orientation — directory layout, key modules, data flows, technical decisions, and code health. A query + synthesis capability rather than a persistent role; produces a guided reading order and flags gotchas for newcomers. Use when: "give me an architecture walkthrough", "narrate this codebase", "explain how this project is organized", "code navigation", "where should I start reading".
context-engineering
by mikeparcewskiContext window management, token optimization, and memory patterns for efficient multi-agent systems. Use when: optimizing token usage in an agentic pipeline, designing memory scope for short / long-term / episodic state, or applying a context-loading strategy (anticipatory / JIT / hybrid).
wicked-brain-review
by mikeparcewskiBrowse stored memories with filters on type, tier, and recency. Read-only — use wicked-brain:forget to archive or wicked-brain:agent dispatch consolidate to promote. Use when: "review my memories", "browse decisions", "what have I stored", "list recent gotchas", "brain review".
wicked-brain-confirm
by mikeparcewskiConfirm or contradict a brain link, adjusting its confidence score. Increases confidence when a link is confirmed by evidence, decreases it when contradicted. Tracks evidence_count for audit purposes. Use when: "confirm this link", "contradict this connection", "adjust link confidence", "mark link as confirmed", "this link is wrong".
wicked-brain-lint
by mikeparcewskiCheck brain health and fix issues. Dispatches a lint subagent that runs deterministic checks (broken links, orphans, stale entries) and semantic analysis (inconsistencies, gaps, tag misalignment). Use when: "lint the brain", "check brain health", "brain lint", "find issues in the brain".
wicked-brain-query
by mikeparcewskiAnswer questions by searching and synthesizing brain content. Dispatches a query subagent that searches, reads, follows links, and produces a cited answer. Use instead of Agent(Explore) or reading files directly for any conceptual question: "what does X do", "how does Y work", "explain Z", "tell me about W", "why does X happen", "give me context on Y", or any question that could be answered from indexed knowledge. Always try this before exploring the codebase manually.
translate
by mikeparcewskiLocale-aware deck translation — translates slide text while preserving layout, styling, and visual structure. Works on HTML slides (post-generate, pre-convert). Handles character length constraints, do-not-translate lists, RTL languages, and CJK font overrides. Use when: "translate", "translate to", "in French", "in Spanish", "localize", "multilingual", "foreign language", "international version", "translate deck", "localize presentation"
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.