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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qual-coherence-check
by linxuleExamine philosophical assumptions and check for methodological coherence. Use when users question their philosophical stance, their language contradicts their declared epistemology, they are moving between stages and want to verify coherence, or something feels 'off' about their analytical approach. Checks language coherence, method-epistemology alignment, and AI relationship consistency.
paradox-navigation
by linxuleThis skill should be used when patterns seem contradictory but both feel true, user is stuck in either/or thinking, theoretical and empirical streams seem irreconcilable, or user mentions 'paradox', 'tension', 'contradiction', 'both/and'. Helps integrate opposites at a higher level.
project-setup
by linxuleThis skill should be used when users want to initialize a new qualitative research project, mentions 'setup', 'initialize', 'new project', 'getting started', or asks about establishing philosophical foundations and epistemic stance. Triggers on phrases like 'start my research project', 'create a new study', 'configure my stance'.
coherence-check
by linxuleThis skill should be used when users question their philosophical stance, their language contradicts their declared epistemology, they are moving between stages and want to verify coherence, mentions 'assumptions', 'examine', 'coherent', 'consistent', or something feels 'off' about their analytical approach.
qual-coding
by linxuleDialogical coding for qualitative research. Acts as thinking partner in Stage 1 (never suggests codes, only asks questions) and reflexive coding partner in Stage 2 (4-stage visible reasoning). Use when coding interview transcripts, field notes, or other qualitative data.
qual-init
by linxuleInitialize a new qualitative research project with Socratic onboarding. Guides researchers through philosophical foundation establishment via interactive dialogue. Use when starting a new project, setting up epistemic stance, or configuring methodological preferences.
gioia-methodology
by linxuleThis skill should be used when users are building or refining their Gioia data structure, mentions 'Gioia', 'data structure', 'themes', 'concepts', 'dimensions', '1st-order', '2nd-order', 'aggregate', or needs to validate/export their analytical hierarchy for publication.
methodological-rules
by linxuleThis skill should be used when users mention 'generate rules', 'isolation rules', 'methodology preset', 'apply preset', 'saturation', 'am I saturated', 'branch', 'fork', 'explore alternative', 'team', 'add researcher', 'intercoder reliability', 'dashboard', 'show status', or after /qual-design completes.
deep-reasoning
by linxuleThis skill should be used when users need to think through a complex analytical decision, asks 'how should I approach this?' or 'help me think through...', is stuck on a difficult coding boundary, needs to plan dimensional analysis, or is building theoretical frameworks in Stage 3.
garden-tending
by linxuleTend the knowledge garden — diagnose vault health, condense project memos into `_project.md` overviews, create/merge cross-project topics, fix broken links, archive superseded notes, extend trails. Trigger on "tend the garden", "condense", "update project overview", "check vault health", "where are we with X?", "what does this project know?", "extend a trail", or when a `_project.md` is empty/stale, a project has 5+ unprocessed memos, or a concept appears in 2+ projects. For autonomous tending with judgment, prefer curator-practice.
research-partner
by linxuleThis skill should be used when a researcher wants to think through ideas, discuss a paper, get feedback on arguments, explore connections, brainstorm, or needs intellectual engagement. Also covers research self-portrait synthesis — surfacing patterns across a researcher's own recent work ('show me my research patterns', 'what have I been working on', 'give me a mirror', '/carrel-mirror'). Triggers on 'help me think', 'what do you think', 'push back', 'what am I missing', 'I'm stuck', 'explore connections', 'mirror', 'self-portrait'.
convert
by linxuleThis skill should be used when a researcher wants to convert a PDF, Word document, PowerPoint, spreadsheet, or image to markdown. Triggers on 'convert', 'import', 'add this paper', a dropped file path, or any mention of PDF/DOCX/PPTX/XLSX conversion.
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.