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.
Querying local SQLite index...
blazediff
by teimurjanRun, author, or update BlazeDiff visual regression tests. Trigger on "visual test", "screenshot regression", "blazediff", "/blazediff".
recall-global
by teimurjanSearch memories across ALL registered lethe projects (cross-repo). Use when the user references work in another repo, asks "what did we do in project X", compares notes across projects, or when the single-project recall skill returned nothing relevant. For same-project recall, use the recall skill instead.
recall
by teimurjanSearch memories from past Claude Code sessions in the CURRENT project. Use when the user's question could benefit from historical context, past decisions, debugging notes, architectural choices, or prior conversations in this repo. For cross-project recall, use the recall-global skill instead.
recall-global
by teimurjanSearch memories across ALL registered lethe projects (cross-repo). Use when the user references work in another repo, asks "what did we do in project X", compares notes across projects, or when the single-project recall skill returned nothing relevant. For same-project recall, use the recall skill instead.
recall
by teimurjanSearch memories from past Codex CLI sessions in the CURRENT project. Use when the user's question could benefit from historical context, past decisions, debugging notes, architectural choices, or prior conversations in this repo. For cross-project recall, use the recall-global skill instead.
blazediff
by teimurjanRun, author, or update BlazeDiff visual regression tests. Trigger on "visual test", "screenshot regression", "blazediff", "/blazediff".
dev-tunnel
by teimurjanStart a local JS/TS dev server and expose it through an ngrok tunnel so the user can test it from a phone, another machine, or while away from their laptop. Trigger when the user says things like "expose my dev server", "ngrok this", "tunnel localhost", "share my local app", "test on my phone", "make my app reachable from outside", "I want to test this remotely", "give me a public URL for my dev server", or any variant where they want a public URL pointing at their local JS app. Also trigger when they reference remote-connect / SSH-from-phone scenarios and need a way to hit their dev server. Don't wait for the exact word "ngrok" — any "expose / share / tunnel / public link / mobile preview" intent in a Node/JS project should trigger this.
address-pr-review
by teimurjanFetch and address GitHub PR review comments for the current branch. Trigger when: user asks to "address PR comments", "fix review feedback", "handle PR reviews", "review-pr", "address review", "fix PR comments", or any request to act on pull request review feedback. Also trigger when the user pastes a PR URL and asks to address its comments.
browser-testing
by teimurjanFull browser automation via Agent Browser Protocol (ABP). Navigate, click, type, scroll, drag, screenshot, extract text, handle dialogs/downloads/file pickers, manage tabs, control JS execution. Single CLI tool.
code-graph
by teimurjanUse tree-sitter to build a structural code graph of a project — extract definitions, references, imports, and module dependencies to quickly understand unfamiliar codebases. Trigger when: user asks to "understand this codebase", "map the architecture", "show me the structure", "what does this project do", "how is this organized", "code graph", "dependency graph", "call graph", or any request to get a high-level overview of a project's code. Also trigger when exploring a new/unfamiliar repo for the first time, or when the user asks about relationships between modules, files, or components. Also trigger when the user asks to refactor a module, file, or component — understanding the module's public surface, internal structure, and all dependents is a prerequisite to safe refactoring.
perf-engineering
by teimurjanPerformance engineering guidance for CPU and memory optimization across languages (Rust, C/C++, TypeScript/JavaScript, Go, Python). Use this skill whenever the user asks about optimizing code performance, reducing memory allocations, improving cache locality, SIMD/vectorization, profiling, benchmarking, or any question about making code faster or more memory-efficient. Also trigger when the user mentions: hot loops, allocation pressure, cache misses, false sharing, memory pools, arena allocation, string interning, branch prediction, auto-vectorization, zero-copy, AoS vs SoA, data-oriented design, or profiling tools (perf, flamegraph, Instruments, VTune, cachegrind). Trigger even for indirect performance questions like "why is this slow", "this function is a bottleneck", "how to reduce memory usage", or "should I optimize this".
topics-rss
by teimurjanFetch the latest entries from all configured RSS/Atom newsletters and blogs (SWE + AI feeds — JavaScript Weekly, TLDR AI, Import AI, Pragmatic Engineer, Simon Willison, OpenAI Blog, etc.). Use when the user wants "newsletter signals", "what did newsletters publish this week", or curated long-form ideas for a LinkedIn post. Returns JSON.
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.