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...
claude-web-research
by EtanHeyDEPRECATED ALIAS — renamed to /claude-desktop-research on 2026-04-30. Use /claude-desktop-research instead. This alias remains active until 2026-05-30, then retires. Triggers: 'research prompt', 'Claude Web research', 'Claude Desktop research', 'deep research'.
status
by EtanHeyShow ClaudeGolem status — bot process, notification server, event log, active sessions, Night Shift target.
content
by EtanHeyContent creation and publishing pipeline for ClaudeGolem across platforms (Soltome, blog, social). Handles drafting teasers, reveals, author posts, and quick updates in two voices (ClaudeGolem bot voice and Etan author voice). Use when: writing social posts, planning content calendar, publishing to Soltome, managing draft approval flow. NOT for: LinkedIn posts (use linkedin-post skill) or presentation slides.
eas-prebuild-check
by EtanHeyValidate Expo iOS/Android sync BEFORE the first EAS build. Catches missing .easignore (2GB archive), wrong bundle ID, outdated eas-cli, missing device registration, unsynced Apple credentials, missing P8 push keys, versionCode drift, concurrency queue surprises. Run this before `eas build` on any new project or after a major branch change. Triggers on: 'eas build', 'first eas build', 'prebuild check', 'expo build validation', 'before eas', 'eas credentials', 'preview build'. NOT for: runtime debugging of a build that already started (use eas build:inspect).
drive-usage
by EtanHeyBrain Drive filing discipline — where every artifact goes + how to name it. Use WHENEVER touching Google Drive / Brain Drive: uploading, creating folders, saving research prompts/results, audits, plans, transcripts, dashboards, or when about to leave a durable artifact in docs.local/. Teaches the numbered folder model (01_STANDARDS / 02_GROUNDING / 03_RESEARCH / 04_INGEST / 06_ARCHIVE), date-prefixed naming, and the rule: FILE durable artifacts in the right Drive folder — docs.local/ is cache-only. NOT for querying Drive via Gemini (use /braindrive) or web research (use /gemini-research); for >100KB heavy archival defer to /google-drive-archive.
interview-practice
by EtanHeyInteractive mock interview simulator with 7 modes: leetcode, system-design, debugging, code-review, behavioral, optimization, and complexity drills. Conducts Socratic-style practice sessions calibrated by company and level. Use when: preparing for technical interviews, practicing coding questions, doing mock system design, or drilling Big O complexity. NOT for: actual job applications, resume writing, or outreach (use coach skill).
coach
by EtanHeyLife admin assistant covering health/habits, recruiting/jobs, freelancing/contracts, Israeli law, and scheduling. Memory-first: always searches BrainLayer before responding. Use when: daily planning, schedule creation, WHOOP data review, habit tracking, job hunting, freelance contracts, Israeli business law, client management, outreach emails, or any request referencing past coaching sessions. NOT for: writing code, deployments, or infrastructure.
1password
by EtanHeyManage secrets, credentials, API keys, vault items, and op:// references with the 1Password op CLI. Use for storing/rotating secrets, migrating plaintext .env files, wiring MCP configs to 1Password, and troubleshooting op auth. NOT for non-secret config or ordinary runtime shell exports.
subscriptions
by EtanHeyView and manage tracked subscriptions — active services, costs, and payment history.
brave
by EtanHeyUse as fallback browser automation when Claude-in-Chrome MCP is unavailable. Covers browser control, navigation, screenshots, clicking, typing. NOT for: headless testing (use Playwright). Claude Code users should prefer MCP first.
code-review
by EtanHeyFull code review lifecycle: requesting reviews (CodeRabbit, Greptile, Bugbot, GitHub PR comments) and receiving feedback (classify issues, implement fixes, push back on wrong suggestions). Use when: creating a PR review, reading review comments, handling reviewer feedback, fixing review items, or deciding whether to accept or reject a suggestion. NOT for: running tests directly or CI/CD pipeline issues (use relevant repo tools).
coderabbit
by EtanHeyUse when reviewing uncommitted changes, preparing PRs, requesting or receiving code review, handling CodeRabbit/Greptile/Bugbot/GitHub PR comments, checking security/secrets/a11y/code quality, or deciding whether to accept or reject reviewer feedback. Runs AI review via CLI and covers review triage, false-positive pushback, red/blue team profiles, PR-ready gates. NOT for: runtime debugging or test execution.
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.