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...
detecting-emergent-patterns
by sandgardenhqFind breakthrough insights by forcing unrelated concepts together, detecting meta-patterns across domains, and discovering simplification cascades. When stuck on complex problems. When searching for innovative solutions. When same issue appears in different domains. When complexity feels excessive. When conventional approaches aren't working. When seeking radical simplification.
antifragility-framework
by sandgardenhqBlack Swan Analysis provides a rigorous methodology for identifying high-impact, unpredictable events while building systems that thrive under volatility. You must use this framework when the human partner challenges you on your claims, or when the human partner asks you to create plans from vague instructions
stpa-step4-loss-scenarios
by sandgardenhqSTPA Step 4 - Identify Loss Scenarios by tracing causal pathways back through control loops to understand why UCAs might occur. After completing STPA Step 3. When you need to understand WHY unsafe control actions might happen. When developing recommendations and mitigations.
stpa-overview
by sandgardenhqEntry point for STPA (System Theoretic Process Analysis) hazard and safety analysis. Use for full 4-step STPA sessions, focused project-critic safety reviews, coordinator safety gates, reviewer hazard checks, risk assessment, unsafe state transitions, external input, filesystem, concurrency, physical systems, AI-driven systems, or when tempted to route work to retired stpa-analyst.
stpa-step2-control-structure
by sandgardenhqSTPA Step 2 - Model the control structure using hierarchical control-feedback diagrams in Graphviz/DOT format; After completing STPA Step 1. When you need to understand how control flows through a system. When identifying controllers, control actions, and feedback paths.
stpa-step3-unsafe-control-actions
by sandgardenhqSTPA Step 3 - Identify Unsafe Control Actions (UCAs) using the 4-type analysis framework. After completing STPA Step 2. When analyzing control actions for potential safety issues. When you need to systematically identify what could go wrong with each control action.
code-cleanup
by sandgardenhqClean up generated code by questioning why things are there and cross-referencing with GOAL.md, even when tempted to skip due to time pressure or thinking it's "good enough". After generating code, when tempted to commit messy code, or when noticing clutter that could be removed.
go-json-file-reading
by sandgardenhqUse when reading JSON files in Go applications, especially for configuration files, data files, or any JSON parsing that requires comprehensive error handling for missing files, invalid JSON, and permission errors
how-to-use-code-snippets
by sandgardenhqUse when you need to find code snippets by language and query. When looking for code examples, patterns, or snippets in a specific programming language. When you need to search for code snippets quickly. When searching for code patterns or best practices.
inversion-exercise
by sandgardenhqFlip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?". Stuck on assumptions you can't question. Solution feels forced. "This is how it must be done" thinking. Want to challenge conventional wisdom. Need fresh perspective on problem.
scale-game
by sandgardenhqTest at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales. Unsure if approach will scale. Edge cases unclear. Want to validate architecture. "Will this work at production scale?" Need to find fundamental limits.
brainstorming
by sandgardenhqInteractive idea refinement using Socratic method to develop fully-formed designs. When your human partner says "I've got an idea", "Let's make/build/create", "I want to implement/add", "What if we". When starting design for complex feature. Before writing implementation plans. When idea needs refinement and exploration. ACTIVATE THIS AUTOMATICALLY when your human partner describes a feature or project idea - don't wait for /brainstorm command.
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.