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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cwmem
by ThomasRohdeRecord decisions, changes, events into cwmem — repo-native institutional memory. Use in any git-managed repository. Triggers: meaningful changes (architecture decisions, process changes, meeting outcomes), user asks to "remember"/"record"/"log"/"track", mentions of "cwmem"/"institutional memory"/ "decision log"/"knowledge base", or CLAUDE.md/AGENTS.md instructions. Proactively offer to record important decisions.
strands-spec
by ThomasRohdeExpert guidance for creating strands-cli workflow specifications (YAML/JSON). Use when creating, modifying, or troubleshooting strands-cli specs for: (1) Multi-step agent workflows (chain, routing, parallel, graph patterns), (2) Tool configuration (python_exec, http_request, custom tools), (3) Runtime and provider setup (Bedrock, OpenAI, Ollama), (4) Input/output handling and templating, or (5) Debugging validation errors
debug-a0
by ThomasRohdeThis skill should be used when the user asks to "debug an A0 program", "fix A0 error", "understand A0 error", "A0 diagnostic", "A0 exit code", "read A0 trace", "why does my A0 program fail", "A0 parse error", "A0 capability denied", "A0 runtime error", or needs to diagnose, understand, or fix issues in existing A0 source code.
write-a0
by ThomasRohdeThis skill should be used when the user asks to "write an A0 program", "create an A0 script", "generate A0 code", "write a .a0 file", "A0 syntax", "A0 example", "how to write A0", "A0 language", or needs to produce any new A0 source code. Provides the complete syntax, type system, tool signatures, stdlib, and idiomatic patterns needed to author correct A0 programs.
apply-rubric
by ThomasRohdeApply an EAROS rubric to an architecture artifact using the three-pass agent evaluation pattern (Extractor, Evaluator, Challenger). Use this skill whenever the user wants to "evaluate an architecture artifact", "apply a rubric", "review an architecture document", "score an architecture artifact", "run an EAROS evaluation", "assess architecture quality", "apply the solution architecture rubric", "evaluate this ADR", "review this capability map", "check this against the rubric", "run the architecture review", or mentions "evaluate", "score", "assess", "review", or "apply rubric" in the context of applying an EAROS rubric to a specific artifact. Also triggers when the user says "how does this artifact score", "is this architecture document good enough", "run the three-pass evaluation", "extract evidence from this document", or any request to systematically evaluate a specific architecture work product against defined criteria. Does NOT trigger for creating rubrics (use earos-rubric for that), general architectur
earos-calibrate
by ThomasRohdeRun EAROS calibration exercises to validate rubric reliability before production use. Use this skill whenever someone wants to calibrate a rubric, validate inter-rater reliability, compare scores against gold-standard artifacts, measure scoring consistency, or says "calibrate this rubric", "run calibration", "check if the rubric is reliable", "compare my scores to the gold set", "test this profile against examples", "is this rubric ready for production", "what is our kappa", "measure agreement between reviewers", "validate a new profile", or "how well does the rubric score consistently". Calibration is required before any new profile can move from draft to candidate status.
kc
by ThomasRohdeUse kc to maintain repo-local knowledge workspaces, ingest local or snapshotted remote sources, register and refresh revisioned source ranges, answer natural-language queries with grounded citations to original sources, prepare durable context packs, write cited artifacts, validate exact kc citation tokens, rewrite or repair legacy citations, diff and apply artifacts safely, lint knowledge state, run retrieval evals, and manage durable task workflows.
drawio-diagram-creation
by ThomasRohdeThis skill should be used when the user asks to "create a diagram", "make a flowchart", "generate a .drawio file", "draw.io diagram", "diagrams.net", "architecture diagram", "sequence diagram", "ER diagram", "class diagram", "network diagram", "org chart", "workflow diagram", "UML diagram", "ArchiMate diagram", "C4 diagram", "C4 model", "enterprise architecture", or mentions "drawio", "mxGraph", or diagram visualization. Provides comprehensive knowledge for creating production-ready DrawIO XML files.
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.