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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gx-act
by opencueRun GitHub Actions workflows locally with nektos/act before pushing, so CI failures are caught on the laptop and the PR can be squash-merged on the first remote run.
cue-agent-profile-manager
by opencueWhen user asks about managing Claude Code or Codex profiles, skills, MCPs, or agent configuration, use cue CLI commands to help them
hydra
by opencueMulti-perspective code review council: advisors analyze, reviewers cross-examine, chairman synthesizes verdict. USE for: architecture decisions, security audits, tradeoff analysis, "what am I missing" questions, pre-merge deep reviews, iterative re-reviews after fixes. DO NOT USE for: simple code generation, syntax fixes, single-file refactors, or factual lookups. TRIGGERS: 'hydra', 'hydra this', 'hydra review', 'run hydra', 'hydra deep', 'Hydra starten', 'hydra iterate', 'hydra re-review', 'hydra follow-up', 'hydra history', 'hydra pr', 'hydra branch', 'hydra ?', 'hydra auto', 'fix #', 'verify', 'hydra explain', 'hydra details', 'hydra tensions', 'hydra blind-spots'.
fallow
by opencueCodebase intelligence for JavaScript and TypeScript. Free static layer reports quality, changed-code risk, cleanup opportunities (unused files, exports, types, dependencies), code duplication, circular dependencies, complexity hotspots, architecture boundary violations, and feature flag patterns. Runtime coverage merges production execution data into the same health report for hot-path review, cold-path deletion confidence, and stale-flag evidence - a single local capture is free, while continuous/cloud runtime monitoring is paid. 97 framework plugins, zero configuration, sub-second static analysis. Use when asked to analyze code health, audit PR risk, find cleanup opportunities or unused code, detect duplicates, check circular dependencies, audit complexity, check architecture boundaries, detect feature flags, clean up the codebase, auto-fix issues, merge runtime coverage, or run fallow.
free-image-and-video-generation
by opencueFree local AI image and video processing toolkit with cloud AI generation. Local tools: upscale (Real-ESRGAN), face enhance (GFPGAN/CodeFormer), background remove (rembg), object erase (LaMa), face swap (InsightFace), segment (FastSAM), media process (FFmpeg). Cloud tools: AI image/video generation via Atlas Cloud API (300+ models). For cloud generation, ALWAYS first use Atlas Cloud MCP tools (atlas_list_models, atlas_get_model_info) to find the model ID and parameter schema, then call scripts/ai-generate.py with the correct --model and parameters. Use when user asks to process, enhance, upscale, generate, or edit images/videos.
cargo-nextest
by opencueUse when running Rust tests slowly with cargo test, or when CI test time hurts. Drop-in faster runner with better output, retries, and JUnit XML.
entroly
by opencueUse when the user mentions "entroly", context compression, prompt-token reduction, hallucination detection, or asks how to cut Claude API spend on long sessions. Points at juyterman1000/entroly and explains how it composes with the caveman + RTK token-discipline lane the user already runs.
cue-rec
by opencueRecord the current desktop/terminal session to a video for marketing, demos, or sharing a flow. Wraps GNOME Shell's built-in Screencast over D-Bus on Wayland (no extra deps beyond ffmpeg + slurp). Use when the user says "record this session", "screencast this", "make a marketing video", "make a demo video", "start recording", "stop the recording", "capture this flow", or invokes /cue-rec.
cue-dashboard
by opencueUse to inspect or drive the running cue studio dashboard: resolve a profile's skills/MCPs, find trigger gaps, or add an MCP to a profile. Via the `cue dash` CLI or the cue-dashboard MCP.
cue-developer
by opencueBuild, test, debug, and contribute to cue itself, the TypeScript/Bun profile CLI for Claude Code and Codex. Use when developing cue: skills, MCPs, profiles, resolver/materializer, or PRs.
integrity-tags
by opencueExplains cue's 7-tag confidence system (VERIFIED, KNOWN, INFERRED, ASSUMED, GUESSED, STALE, UNKNOWN) used to label every research- or decision-relevant claim. Use when user says "what does VERIFIED mean", "explain the colored tags", "what's the confidence system", "why is this yellow", "what does [ASSUMED] mean", or asks about cue's integrity protocol.
profile-optimizer
by opencueRuns cue optimizer and rank commands, presents visual results, suggests removals and additions to slim the active profile. Use when user says "optimize profile", "clean up skills", "what skills am I not using", "suggest skills for this repo", or "profile review".
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.