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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frontmatter
by a3lemManage AI provenance metadata blocks in source files. Use to stamp files with review status and rule references, or scan directories for coverage. Triggers on: "stamp", "frontmatter", "ai-frontmatter", "mark as reviewed", "scan reviewed".
atcha
by a3lemSend and receive messages with other AI users working in parallel.
project-knowledge
by a3lemRoutes project knowledge to the right store. Use when deciding where to put information, looking up existing knowledge, or understanding the relationship between specs, notes, decisions, journal entries, and docs. Triggers on: "where should I put this", "where do I find", "knowledge", "documentation strategy", "what goes where".
decision-log
by a3lemRecord and look up decisions in notes/DECISIONS.md with dcn-xxxx reference codes. Use for logging architectural choices, design decisions, and team agreements, or finding past decisions. Triggers on: "log a decision", "we decided", "record this decision", "decision log", "what did we decide about", "find decision".
journal
by a3lemThis skill should be used when the user wants to write a journal entry, log an observation, capture a discovery, record a design insight, or note a lesson learned. Entries are timestamped and stored in notes/journal/. Triggers on: "journal", "write a journal entry", "log this", "observation", "I learned", "note to self", "capture this for later".
project-notes
by a3lemThis skill should be used when creating, updating, or organizing pages and documentation in the notes/ directory. It covers notes/ conventions, INDEX.md curation, wikilinks, and routing guidance for choosing between notes pages, journal entries, and decision log entries. Triggers on: "write a note", "document this", "create a page about", "add to notes", "where should I document this", "what type of note should this be".
spec-driven-development
by a3lemSpec-driven development workflow. Use for exploring ideas, proposing changes, implementing specs, and archiving completed work. Triggers on "spec", "propose", "explore", "apply", "archive", "critique", or "specification".
theo-calvin-testing
by a3lemDifferential testing with Theodore Calvin's framework (tc). Use when writing tc tests, reasoning about test scenarios, creating input/expected JSON pairs, or debugging test failures.
ticket
by a3lemThis skill should be used when the user asks to "create a ticket", "add a task", "track an issue", "manage dependencies", "show blocked tickets", "list open tickets", "close a ticket", "add notes to a ticket", "link tickets", "query tickets", "what's ready to work on", "what's blocking", "break down an epic", or any task management operation using the `tk` CLI. Also triggers when the user mentions "tk", "ticket system", ".tickets", or asks about project task organization.
spexl-apply
by a3lemImplement a proposed spec change -- write the code and the tests that satisfy every requirement and scenario. Use when the user asks to "apply", "implement", "build the feature", "start coding the spec", "make it real", or references an active change that is ready for implementation. Never claim "done" without passing tests. Use `/spexl-archive` next to merge deltas into reference specs once the change is complete.
spexl-archive
by a3lemArchive a completed spexl change by merging its deltas into reference specs and moving the change to the archive directory. Use when the user asks to "archive", "merge deltas", "finalize", "complete a change", "wrap up", or references a change that has been applied and verified. Only archive when implementation is done and tests pass -- archiving a half-finished change leaves the reference specs inconsistent.
spexl-explore
by a3lemExplore an idea, investigate a problem, or clarify requirements before committing to a formal spec change. Use when the user asks to "explore", "investigate", "think through", "research", "brainstorm", "understand the codebase", or wants a thinking partner before writing a proposal. Produces no artifacts by default -- this is discussion, diagrams, and code reading, not implementation. Transition to `/spexl-propose` when a decision crystallizes.
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.