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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docs-validator
by C0ntr0lledCha0sDocumentation quality validator for Logseq Template Graph. Checks documentation completeness, accuracy, formatting, links, and consistency. Activates when asked to "validate docs", "check documentation", "audit docs quality", "find broken links", or similar requests. Provides actionable feedback and specific fixes for documentation issues.
commit-helper
by C0ntr0lledCha0sExpert conventional commits assistant for the Logseq Template Graph project. Analyzes git changes and generates proper conventional commit messages with correct type, scope, and format. Use when the user needs help writing commits or validating commit messages.
github-issues
by C0ntr0lledCha0sGitHub issues management assistant for Logseq Template Graph. Analyzes issues, triages with labels, plans implementations, generates responses, creates PRs, and manages issue lifecycle. Use when handling bug reports, feature requests, questions, or coordinating development through GitHub issues.
edn-analyzer
by C0ntr0lledCha0sDeep EDN template analyzer for Logseq database graphs. Analyzes template structure, counts classes/properties, finds orphaned items, checks quality, and compares variants. Use when analyzing template files, finding issues, or comparing different template versions.
schema-research
by C0ntr0lledCha0sSchema.org research assistant for Logseq Template Graph. Investigates Schema.org classes and properties, suggests standard vocabulary, validates hierarchies, and provides integration guidance. Use when adding new classes/properties, researching Schema.org standards, or planning template expansions.
documentation-writer
by C0ntr0lledCha0sExpert technical writer for Logseq Template Graph project. Generates comprehensive, accurate, and well-structured documentation for modules, features, guides, and APIs. Activates when asked to "write docs", "document this", "create README", "update documentation", or similar requests. Analyzes code/templates to extract information and writes clear, user-focused documentation following project style.
module-health
by C0ntr0lledCha0sModular architecture health assessor for Logseq Template Graph. Analyzes module balance, cohesion, size distribution, and dependencies. Calculates health scores and suggests reorganization. Use when checking module structure, assessing architecture quality, or planning refactoring.
coordinating-projects
by C0ntr0lledCha0sAutomatically activated when user mentions multi-project coordination, cross-project dependencies, portfolio management, roadmap planning, resource allocation across projects, or asks to coordinate/manage multiple projects simultaneously. Provides strategic project coordination expertise.
triaging-issues
by C0ntr0lledCha0sGitHub issue triage and management expertise. Auto-invokes when issue triage, duplicate detection, issue relationships, or issue management are mentioned. Integrates with existing github-issues skill.
writing-to-logseq
by C0ntr0lledCha0sExpert in writing data to Logseq DB graphs via HTTP API. Auto-invokes when users want to create pages, add blocks, update content, set properties, or sync conversation notes to their Logseq graph. Provides CRUD operations with safety guidelines.
suggesting-improvements
by C0ntr0lledCha0sExpert at suggesting specific, actionable improvements to Claude's responses and work. Use when Claude's output needs enhancement, when quality issues are identified, or when iterating on solutions.
planning-sprints
by C0ntr0lledCha0sAutomatically activated when user mentions sprint planning, backlog refinement, iteration planning, sprint goals, capacity planning, velocity tracking, or asks to plan/start/close a sprint. Provides comprehensive sprint planning expertise using agile best practices.
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.