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...
verticals
by rushindrasinhaAI-native vertical video engine with niche intelligence. Takes a one-line topic and a niche profile, and outputs a finished YouTube Short/Reel/TikTok with AI-generated b-roll, voiceover, burned-in captions, background music, and thumbnail. Supports multiple LLM providers (Claude, Gemini, GPT, Ollama), TTS providers (Edge TTS, ElevenLabs, MiniMax, 60db), and 15+ content niches.
cricd-prompt-standard
by rushindrasinhaThe CRICD framework for structuring sub-agent task prompts — Context, Relevance, Instruction, Constraints, Demonstration. Produces dramatically better output from any LLM sub-agent. Use when spawning sub-agents, writing task prompts, or structuring complex instructions for AI models.
critique-loop-protocol
by rushindrasinhaFormal protocol for draft → critique → revise loops in AI agent workflows. Defines when to use critique loops (money, public publishing, strategy) and when to skip (simple edits, routine coding). Prevents both over-engineering simple tasks and under-reviewing critical ones.
gateway-watchdog
by rushindrasinhaMonitor OpenClaw Gateway health by tailing logs for error patterns — 429 rate limits, auth failures, delivery failures, timeouts. Alert when error rate crosses thresholds. Use when setting up production monitoring for an OpenClaw Gateway deployment, or when a user says "watch the gateway", "monitor gateway errors", "alert me on gateway failures".
gstack-coding-discipline
by rushindrasinhaA 5-tier dispatch framework for coding tasks in AI agent workflows. Routes tasks from SIMPLE (one-file typo fix) to FULL (multi-day feature build) with appropriate rigor at each level. Prevents over-engineering simple tasks and under-planning complex ones.
indic-language-translator
by rushindrasinhaUse when a user wants to translate text to or from any Indian language — Hindi, Tamil, Telugu, Bengali, Marathi, Kannada, Malayalam, Gujarati, Punjabi, Urdu, Odia, Assamese, Sanskrit — or says "translate this message", "translate this to Hindi", "what does this say in Tamil", "convert to Marathi", or similar. Also handles global languages (Spanish, French, Arabic, Chinese, Japanese, Korean, etc.). Uses Gemini 2.5 Flash. Also use when asked to detect what language a piece of text is written in.
song-identifier
by rushindrasinhaUse when a user sends an audio file and asks "what is this song?", "identify this track", "shazam this", or similar. Accepts any audio format (OGG, MP3, WAV, M4A). Returns title, artist, album, release date, and Spotify/Apple Music links via the AudD API.
system-state-injection
by rushindrasinhaInject live system health into agent context at session open. Cron script checks integrations, disk, gateway status, active sessions — writes compact JSON. Agent reads it before answering any status question. Eliminates stale-recall failures where agents answer from memory instead of live state. Use when setting up always-current system awareness for an autonomous agent.
ai-agent-kill-switch
by rushindrasinhaPhrase-triggered emergency stop system for AI agents. Instantly halts all agent automation when a trigger phrase is detected in session logs, or via manual flag. Use when setting up a safety kill switch, emergency stop, or pause-all-automation mechanism for autonomous agents. Survives restarts via LaunchAgent watchdog.
twitch-stream-monitor
by rushindrasinhaMonitor Twitch channels going live, auto-record streams with streamlink, optionally upload recordings to Google Drive, and send WhatsApp notifications when streamers go online or finish.
weekly-cost-report
by rushindrasinhaUse when a user asks for a cost report, model usage summary, token spend breakdown, API costs, or says "how much did I spend this week?", "show me my API costs", "what's my model spend?", "weekly cost summary", or "usage report". Parses OpenClaw JSONL session logs and aggregates per-model token usage and dollar cost for the last 7 days.
weekly-reality-capture
by rushindrasinhaAuto-generate a reality-first weekly log from daily memory logs, git history, and agent state. Uses Claude to synthesize what actually happened — not a strategic filter, not a curated narrative. Supports backfill by week number. Use when a user says "weekly recap", "what happened this week", "weekly capture", or "reality check".
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.