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...
internal-comms
by AutumnsGroveProfessional internal communications creation and management with templates for status reports, newsletters, announcements, team updates, and cross-functional communication. Use for: (1) Weekly/monthly status reports, (2) Company newsletters, (3) All-hands announcements, (4) Team updates, (5) Policy communications, (6) Change management communications, (7) Recognition and celebrations
d3js-visualization
by AutumnsGroveProfessional data visualization creation using D3.js with support for interactive charts, custom visualizations, animations, and responsive design. Use for: (1) Creating custom interactive charts, (2) Building dashboards, (3) Network/graph visualizations, (4) Geographic data mapping, (5) Time series analysis, (6) Real-time data visualization, (7) Complex multi-dimensional data displays
panther-strike
by AutumnsGroveLock in on a single GitHub issue and STRIKE to fix it. High-energy, surgical focus. Prowl, investigate, plan, strike, kill. Use when targeting one specific issue for rapid resolution.
lynx-repair
by AutumnsGroveReview pull request feedback with discerning judgment. The lynx scans comments, identifies legitimate concerns, addresses minor issues directly, and plans responses to significant feedback. Use when responding to PR reviews and addressing reviewer concerns.
crow-reason
by AutumnsGroveCritical reasoning and devil's advocate. Steelmans your position, then finds the cracks. Use when you need assumption-challenging, pre-mortems, red-teaming, or when a plan seems too perfect and nobody is asking the hard questions.
beaver-build
by AutumnsGroveBuild robust test dams that catch bugs before they flood production. The beaver surveys the stream, gathers materials wisely, builds with care, reinforces thoroughly, and ships with confidence. Use when writing tests, deciding what to test, or reinforcing code.
fox-optimize
by AutumnsGroveHunt performance bottlenecks with swift precision. Stalk the slow paths, pinpoint the prey, streamline the code, catch the gains, and celebrate the win. Use when optimizing performance, profiling code, or hunting for speed.
better-auth-best-practices
by AutumnsGroveBetter Auth framework reference — configuration, security, rate limiting, sessions, plugins, and production hardening. Use when configuring Better Auth, auditing auth security, adding plugins, or troubleshooting Heartwood.
bear-migrate
by AutumnsGroveMove mountains with patient strength. Wake from hibernation, gather what must move, carry it carefully, hibernate to verify, and wake again to confirm. Use when migrating anything — data, components, icons, documents, formats, or systems.
osprey-appraise
by AutumnsGrovePrecision project estimator that turns security audits and code assessments into professional proposals with scope, timeline, pricing, and deliverables. The Osprey accounts for what others overlook. Use when quoting remediation work, estimating project scope, or producing client-ready proposals.
cat-check
by AutumnsGroveJudge if your UI feels Grove — warm, intentional, handcrafted. The cat walks into the room, sniffs the air, circles the page, and either curls up or walks away. Use when output looks technically correct but might not feel right, or after any builder finishes UI work.
beaver-build
by AutumnsGroveBuild robust test dams that catch bugs before they flood production. The beaver surveys the stream, gathers materials wisely, builds with care, reinforces thoroughly, and ships with confidence.
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.