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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thinking-via-negativa
by tjboudreauxAbout to add a feature/layer/process to fix a problem. First ask what to remove instead — subtraction is often more robust than addition. Use for simplification and complexity reduction.
thinking-red-team
by tjboudreauxUse when reviewing code, auth, or APIs for security vulnerabilities — adopt an attacker mindset, enumerate the attack surface, report only findings with a concrete reproducible attack path.
thinking-regret-minimization
by tjboudreauxWhen advising a human on a high-stakes, hard-to-undo life/career choice, weigh the asymmetry between a recoverable downside and a permanent missed opportunity, not just the short-term cost.
thinking-reversibility
by tjboudreauxBefore deliberating over a decision, ask if it's a one-way door (costly to undo) or two-way (cheap to undo) — decide two-way doors fast, and look for moves that make one-way doors reversible.
thinking-scientific-method
by tjboudreauxUse when a symptom could have several causes and you must find the faulty code by ranking falsifiable hypotheses and checking the cheapest discriminating observation first.
thinking-second-order
by tjboudreauxWhen a change has effects past the immediate fix (incentives, scale, feedback loops), ask "and then what?" across horizons before committing — the obvious fix often backfires downstream.
thinking-socratic
by tjboudreauxUse before building when a request is vague, assumption-laden, or "obvious." Ask the clarifying questions that surface hidden requirements and false premises instead of guessing.
thinking-steel-manning
by tjboudreauxUse before rejecting a proposal or when you're inclined to just agree with the user. Build the strongest version of the opposing case first, then engage that — not a weak version.
thinking-systems
by tjboudreauxUse when debugging across services/an incident where a fix in one place breaks another, or behavior is emergent and no single component explains it. Maps the system and traces causes.
thinking-theory-of-constraints
by tjboudreauxUse when optimizing latency or throughput in a pipeline and one stage dominates—focus all effort on that single bottleneck, since speeding up the others changes nothing until it's fixed.
thinking-thought-experiment
by tjboudreauxYou need to trace how a system would fail or behave at a scale you can't cheaply test or measure. Use to imagine the scenario and walk the consequence chain step by step.
thinking-triz
by tjboudreauxUse when stuck between two architecture or API requirements that seem mutually exclusive — name the contradiction precisely, then separate the conflicting states in time, space, or condition.
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.