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...
two-factor-authentication-best-practices
by EpicenterHQBetter Auth twoFactor plugin: TOTP, OTP, backup codes, trusted devices, and 2FA sign-in. Use when adding MFA, authenticator setup, two-factor enrollment, backup codes, or trusted-device flows.
better-auth-best-practices
by EpicenterHQBetter Auth server/client setup: `auth.ts`, generated schema, DB adapters, sessions, cookies, env vars, and plugins. Use when mentioning Better Auth, betterauth, auth handlers, OAuth, email/password, or session configuration.
better-auth-security-best-practices
by EpicenterHQBetter Auth security hardening: rate limits, secrets, CSRF, trusted origins, cookies, sessions, OAuth tokens, and audit logging. Use when reviewing auth security, brute-force protection, token handling, or deployment safety.
email-and-password-best-practices
by EpicenterHQBetter Auth email/password setup: verification emails, password reset, policies, hashing, and credential sign-in. Use when adding or hardening email/password login, sign-up, reset, or verification flows.
organization-best-practices
by EpicenterHQBetter Auth organization plugin: organizations, members, invitations, roles, teams, and RBAC. Use when adding org setup, team management, member roles, invitations, permissions, or multi-tenant access control.
type-level-error-messages
by EpicenterHQMake compile-time errors readable by branding constraint-violation types as template literal messages with a U+200B zero-width-space suffix. Use when writing helper functions that constrain object keys, string shapes, or other literal-type inputs and you want the TypeScript error tooltip to read as an English sentence pointing at the offending value.
epicenter-ui
by EpicenterHQEpicenter UI component selection and composition patterns for Svelte apps using @epicenter/ui. Use when choosing or reviewing local UI components, loading or empty states, skeletons, spinners, command empty states, action pending UI, table/list no-row states, button or link tooltips, modal/dialog/sheet/drawer surfaces, package import boundaries, wrapper minimization, or replacing ad hoc UI such as Loading... text, custom loading dots, raw animate-pulse placeholders, raw tooltip wrappers, or one-off centered status markup.
elysia
by EpicenterHQElysia.js: error handling, status responses, plugin composition. Use for Elysia, Eden Treaty, API route handlers, HTTP errors, type-safe clients.
define-errors
by EpicenterHQdefineErrors from wellcrafted: variant factories, extractErrorMessage, InferErrors/InferError, call site patterns. Use when creating error types or reviewing error patterns.
fresh-eyes-grill
by EpicenterHQFresh-context adversarial review for staged diffs, state machines, type shapes, lifecycle boundaries, and confusing abstractions. Use when the user says "fresh eyes", "grill this", "why not simpler", "state-machine audit", "does this type shape earn it", or asks for a new-developer review through a subagent. Compose with greenfield-clean-breaks only when compatibility pressure has been explicitly released.
notebook-explanation
by EpicenterHQExplain technical systems in a notebook style: short working notes, small code blocks, ASCII diagrams, concrete examples, and compressed rules. Use when the user asks to understand architecture, APIs, auth flows, specs, boundaries, code ownership, design tradeoffs, or says "zoom out", "give me the bigger picture", "what does this fit into", or "I'm lost in this file".
spec-execution
by EpicenterHQExecute `specs/*.md` plans through working checkpoints. Use when the user says "execute this spec", "implement this plan", "run the spec", or points at a spec file.
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.