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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gluestack-nativewind
by CodySwannGTThis skill enforces Gluestack UI v3 and NativeWind v4 design patterns for consistent, performant, and maintainable styling. It should be used when creating or reviewing components, fixing styling issues, or refactoring styles to follow the constrained design system.
gluestack-nativewind
by CodySwannGTThis skill enforces Gluestack UI v3 and NativeWind v4 design patterns for consistent, performant, and maintainable styling. It should be used when creating or reviewing components, fixing styling issues, or refactoring styles to follow the constrained design system.
atlassian-access
by CodySwannGTVendor-neutral access layer for Atlassian (JIRA + Confluence). Every jira-* and confluence-* skill MUST delegate through this skill rather than calling Atlassian directly. Resolves a substrate per operation in this order: (1) acli if installed and its active profile matches the configured site, (2) Atlassian MCP if authenticated and the configured cloudId is in its accessible resources, (3) curl + API-token Basic auth. Verifies the active connection matches `.lisa.config.json` before every operation — substrates authenticated as a different Atlassian account are skipped, not used.
active-record-model-best-practices
by CodySwannGTBest practices for Ruby on Rails models, splitting code into well-organized, maintainable code. Use when a model exceeds ~100 lines, has mixed responsibilities, or when the user asks to refactor, extract, clean up, or organize a Rails model. Applies patterns: concerns, service objects, query objects, form objects, and value objects.
active-record-model-best-practices
by CodySwannGTBest practices for Ruby on Rails models, splitting code into well-organized, maintainable code. Use when a model exceeds ~100 lines, has mixed responsibilities, or when the user asks to refactor, extract, clean up, or organize a Rails model. Applies patterns: concerns, service objects, query objects, form objects, and value objects.
lisa-coding-agent-parity
by CodySwannGTThis skill should be used when Lisa needs to research feature parity across the coding agents it can install into — Claude Code, Codex, Cursor (cursor-agent), Antigravity (agy), and GitHub Copilot. It produces a four-part research artifact (universal feature catalog, support matrix, plugin-distributability matrix, polyfill designs for the gaps) drawn from both web/documentation research and direct CLI queries. RESEARCH ONLY — implementation lives in the sibling skill `lisa-coding-agent-parity-implement`, which consumes this artifact. Use whenever the agent fleet changes (new CLI added, existing CLI ships a capability), when Lisa needs an updated picture of where parity stands, or before opening any implementation work that touches multiple agents.
typeorm-patterns
by CodySwannGTEnforces TypeORM implementation patterns for this NestJS backend project. This skill should be used when creating or modifying TypeORM entities, repositories, database configuration, migrations, or any database-related code. It covers configuration patterns (TypeOrmModule.forRootAsync, replication, naming strategy), entity patterns (base entity, comments, indexes), and observability (X-Ray logging).
typeorm-patterns
by CodySwannGTEnforces TypeORM implementation patterns for this NestJS backend project. This skill should be used when creating or modifying TypeORM entities, repositories, database configuration, migrations, or any database-related code. It covers configuration patterns (TypeOrmModule.forRootAsync, replication, naming strategy), entity patterns (base entity, comments, indexes), and observability (X-Ray logging).
lisa-wiki-usage
by CodySwannGTUse when explaining how to query, browse, update, maintain, or contribute to the Lisa LLM Wiki.
lisa-wiki-onboard-me
by CodySwannGTOnboard a user to the project via its LLM Wiki. Interviews the user about themselves in relation to the project, captures that to project-scoped memory only, then gives a guided tour of what the project is and sample questions they can ask. Use when someone is new to the project or asks to be onboarded. Read-mostly — it does not open PRs or write PII into the wiki.
lisa-wiki-query
by CodySwannGTAnswer a question from the LLM Wiki with citations. Reads the index, drills into relevant pages, and synthesizes a cited answer. Read-only by default; only files new synthesis back into the wiki when the user explicitly asks. Use when someone asks a question the wiki should be able to answer, or wants to explore what the wiki knows.
lisa-wiki-usage
by CodySwannGTExplain how to browse, query, and contribute to this project's LLM Wiki. Use when a user asks how the wiki works, where knowledge lives, how to find something, or how to add to it — the read/navigation path, not an ingestion or write workflow.
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.