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...
vault-search
by RoasbeefSemantic search and Dataview-style queries across the Obsidian vault. Use when searching for notes by meaning, finding related content, querying frontmatter metadata, or answering questions about vault contents. Trigger phrases include "search vault", "find notes about", "what do I have on", "related notes", "list tasks", "show positions".
variant-analysis
by RoasbeefFind similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.
agent-browser
by RoasbeefBrowser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
agent-cli
by RoasbeefDesign and review CLIs for AI agent consumption. Covers machine-readable output, input hardening against hallucinations, schema introspection, context window discipline, dry-run safety rails, and skill file packaging. Use when building new CLIs, adding agent support to existing CLIs, reviewing CLI designs for agent compatibility, or wrapping APIs as CLI tools. Triggers: agent CLI, CLI for agents, machine-readable CLI, agent-first CLI, CLI agent DX.
agentic-code-reasoner
by RoasbeefThis skill enables deep, execution-free code analysis using the "Semi-Formal Reasoning" methodology. Use it for complex debugging, patch verification, or subtle logic questions where standard inspection might miss edge cases. It requires the generation of a "Reasoning Certificate" verifying logic paths before delivering a conclusion.
eclair
by RoasbeefRun and interact with eclair Lightning Network daemon in Docker. Use for Lightning development, testing payment channels on regtest, managing eclair containers, and calling eclair API endpoints (getinfo, connect, open/close channels, pay/receive).
go-debug
by RoasbeefInteractively debug Go programs in a single context using Delve (dlv) driven through tmux. Use when a bug requires runtime inspection — stepping through code, examining variables, walking goroutines, attaching to a live process, or debugging a hanging integration test — rather than just reading the source. Triggers include "step through this", "set a breakpoint", "attach to the running server", "why is this goroutine stuck", "debug this failing test".
lnd
by RoasbeefRun and interact with lnd Lightning Network daemon in Docker. Use for Lightning development, testing payment channels on regtest, managing lnd containers, and calling lnd RPC endpoints (getinfo, connect, open/close channels, pay/receive). Supports bitcoind, btcd, and neutrino backends.
lnget
by RoasbeefUse lnget to fetch resources from L402-protected URLs that require Lightning payments. Covers basic fetching, payment limits (max cost, max routing fee), token cache management, and Lightning backend status. Use when an HTTP request returns 402 Payment Required and a Lightning micropayment is needed, or when downloading files behind a Lightning paywall.
mutation-testing
by RoasbeefValidates Go test suite quality through mutation testing using go-gremlins/gremlins. Mutates production code, runs the test suite against each mutant, and reports which mutants the tests fail to kill — exposing weak assertions that line coverage cannot detect. Use when evaluating test effectiveness, validating newly written tests, or improving test quality for mission-critical code (consensus, channel state, payment flows, crypto). Triggers: "mutation test", "are these tests strong", "validate test quality", "/mutation-testing".
nano-banana
by RoasbeefGenerate and edit images using Gemini's image generation API (Imagen 3). This skill should be used when creating images, illustrations, diagrams, editing existing images, or iteratively refining visual content through multi-turn conversations.
property-based-testing
by RoasbeefProvides guidance for property-based testing across multiple languages and smart contracts. Use when writing tests, reviewing code with serialization/validation/parsing patterns, designing features, or when property-based testing would provide stronger coverage than example-based tests. For Go code, uses pgregory.net/rapid as the primary PBT framework.
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.