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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rag-patterns
by AgentientVertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus management, retrieval tool creation, and citation extraction. PROACTIVELY activate for: (1) RAG pipeline integration and Vertex AI RAG Engine setup, (2) corpus creation and document ingestion, (3) retrieval tool configuration and grounding metadata parsing. Triggers: "add rag", "rag pipeline", "vertex ai search"
rag-patterns
by AgentientVertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus management, retrieval tool creation, and citation extraction. MUST BE USED for: RAG pipeline integration, Vertex AI RAG Engine setup, corpus creation, document ingestion, retrieval tool configuration, and grounding metadata parsing. Keywords: add rag, rag pipeline, vertex ai search, grounding, corpus, retrieval, citations, RagCorpus, Tool.from_retrieval, grounding_metadata.
red-blue-validator
by AgentientIterative adversarial stress-testing through Red/Blue team dynamics. Red Team generates substantive, steel-manned attacks against propositions; Blue Team responds with defenses, mitigations, and hardening. Cycles continue until convergence criteria are met, producing a battle-tested proposition. PROACTIVELY activate for: (1) High-stakes decisions requiring stress-testing, (2) Strategy validation before major commitment, (3) Architecture decision hardening, (4) Proposal defense preparation, (5) Security posture review, (6) Investment due diligence with adversarial lens. Triggers: "red team this", "blue team", "stress test", "attack this plan", "find weaknesses", "adversarial review", "devil's advocate", "what could go wrong", "poke holes in this", "challenge this decision", "war game this"
write-howto
by AgentientCreate task-oriented documentation for accomplishing specific goals. PROACTIVELY activate for: (1) procedures and recipes, (2) quick reference guides, (3) troubleshooting guides, (4) operational runbooks. Triggers: "write howto", "how to guide", "procedure", "recipe", "quick guide", "step by step", "instructions for"
pydantic-schema
by Agentient[STUB - Not implemented] Pydantic schema definitions for RAG pipeline data models. PROACTIVELY activate for: [TODO: Define on implementation]. Triggers: [TODO: Define on implementation]
pydantic-v2-strict
by AgentientPydantic V2 data modeling with strict mode enforcement for type safety. PROACTIVELY activate for: (1) Creating data models or schemas, (2) Validating API request/response types, (3) Building configuration classes, (4) Defining data contracts, (5) Implementing DTOs. Triggers: "pydantic", "BaseModel", "ConfigDict", "Field", "validator", "model_dump", "strict mode", "data model"
rhf-zod-schema-integration
by AgentientReact Hook Form v7 with zodResolver integration for type-safe validated forms. PROACTIVELY activate for: (1) creating forms with useForm and zodResolver, (2) implementing field registration with spread syntax, (3) handling form state and errors. Triggers: "react hook form", "useForm", "zodResolver"
rhf-dynamic-field-arrays
by AgentientReact Hook Form useFieldArray for dynamic list management with Zod validation. PROACTIVELY activate for: (1) creating dynamic form arrays with add/remove, (2) validating arrays with Zod min/max, (3) using field.id as key. Triggers: "field array", "useFieldArray", "dynamic form"
test-coverage-analysis
by AgentientTest coverage analysis, gap identification, and coverage configuration for Python and JavaScript. PROACTIVELY activate for: (1) Analyzing coverage reports, (2) Identifying critical coverage gaps, (3) Configuring coverage tools, (4) Setting up CI/CD coverage checks, (5) Branch coverage analysis. Triggers: "coverage", "uncovered", "pytest-cov", "istanbul", "c8", "coverage report", "coverage gap"
assumption-validator
by AgentientSystematically surface, classify, and stress-test assumptions in decisions, strategies, and plans. Transforms hidden assumptions into visible, testable propositions with load-bearing analysis and counterfactual validation. PROACTIVELY activate for: (1) Pre-commitment decision reviews, (2) Strategy validation before execution, (3) Investment due diligence, (4) Architecture decision records, (5) Product direction pivots, (6) Risk assessments requiring assumption audit. Triggers: "validate assumptions", "test assumptions", "assumption check", "stress test this decision", "what are we assuming", "pre-mortem", "what could go wrong", "challenge this plan", "devil's advocate"
research-interviewer
by AgentientSystematic knowledge elicitation through structured interviewing with epistemic confidence tracking, MECE coverage verification, and bias-protected questioning. PROACTIVELY activate for: (1) Gather research requirements, (2) Elicit problem statements, (3) Extract domain knowledge, (4) Clarify research goals, (5) Generate requirements through discovery. Triggers: "interview me", "elicit knowledge", "extract information", "research interview", "gather requirements", "conduct interview", "knowledge extraction"
distributed-tracing-context-propagation
by AgentientW3C Trace Context propagation across service boundaries for distributed tracing. PROACTIVELY activate for: (1) Implementing trace context propagation, (2) Cross-service correlation, (3) Setting up traceparent headers, (4) Baggage propagation, (5) Multi-service debugging. Triggers: "distributed tracing", "trace context", "traceparent", "correlation", "span", "baggage", "propagation"
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.