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...
d4
by brycewang-stanfordAgent D4 - Measurement Instrument Developer - Scale construction and psychometric validation. Covers item development, validity evidence, and reliability testing for social science research.
evaporating-cloud
by yogsoth-aiModel conflicts as Goldratt's Evaporating Cloud — expose hidden assumptions behind opposing needs to dissolve the conflict.
factor-enumeration
by yogsoth-aiList all key factors, conditions, and assumptions that support or enable the artifact's conclusion.
governing-variable-surfacing
by yogsoth-aiApply Argyris framework to identify governing variables — the unstated rules driving behavior in a research field.
present-candidates
by yogsoth-aiAnalyze sub-directions within the user's chosen field and present ranked candidates. Combines sub-direction identification, skill-gap matching, and presentation into a single SOP. Depth scales by start mode: cold-start shows broad sub-directions, warm-start shows specific sub-problems, hot-start shows granular technical details.
pricing-psychology-guide
by wentoraiBehavioral economics in pricing strategies and consumer decisions
soc-cognitive-bias
by asgard-ai-platformIdentify and analyze cognitive biases including confirmation bias, anchoring, availability heuristic, and sunk cost fallacy in decision-making contexts. Use this skill when the user needs to audit a decision for bias, understand why a team keeps making the same mistakes, design debiasing interventions, or evaluate whether a conclusion is based on evidence or cognitive shortcuts — even if they say 'are we fooling ourselves', 'why do we keep getting this wrong', or 'is this analysis biased'.
s4h-cognition-attention
by human-avatarMaps the attention economy of a mind or situation — what captures it, what depletes it, and how to protect it. Use when asked 'why can't I focus', 'attention keeps getting pulled away', 'how do I protect deep work', 'what's stealing my focus', 'I keep getting distracted', or when designing environments, workflows, or communications that need to respect cognitive bandwidth.
s4h-cognition-cognitive-load
by human-avatarManages the limits of working memory — chunking, offloading, and reducing unnecessary complexity to free capacity for what matters. Use when asked 'this is too complex to hold in my head', 'I keep losing track of where I am', 'how do I make this simpler to think about', 'the design is overwhelming', 'people aren't retaining this', or when information architecture, learning design, or communication complexity needs to be optimised for working memory limits.
s4h-cognition-mental-models
by human-avatarSurfaces and audits the internal representations that drive perception and decision-making. Use when asked 'what assumptions am I making', 'why do I keep seeing this the same way', 'what model is driving this decision', 'I think I'm missing something about how this works', 'what's my mental model here', or when a belief or decision pattern needs to be examined from the inside.
organization-science
by brycewang-stanfordUse when targeting Organization Science (OrgSci) or deciding whether an organization theory / behavior / innovation manuscript fits this venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics.
honesty-humility
by pjt222Transparencia epistémica — reconocer la incertidumbre, señalar limitaciones, evitar el exceso de confianza y comunicar lo que se sabe, lo que no se sabe y lo incierto con confianza proporcional. Mapea la dimensión de personalidad HEXACO al razonamiento de IA: calibración veraz de la confianza, divulgación proactiva de brechas y resistencia a la tentación de parecer más seguro de lo justificado. Usar antes de presentar una conclusión, al responder preguntas donde el conocimiento es parcial o inferido, después de notar una tentación de declarar información incierta como cierta, o cuando un usuario está tomando decisiones basadas en información proporcionada.
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.