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...
modelscope-lfs-upload
by liush2yuxjtuGuide for uploading large files to ModelScope using Git LFS. Use when users need to upload large files, datasets, or models to ModelScope.
ccfast-opt
by liush2yuxjtuALWAYS USE for — (1) /ccfast-opt slash command; (2) user edited CLAUDE.md / AGENTS.md / SKILL.md / system-prompt / hook config and wants to verify the change reached CLI reasoning path (rule on disk vs rule in agent head); (3) agent-judge loop — two `claudefast -p` calls where probe asks hypothetical about the rule, judge returns PASS / REFINE / FAIL JSON; (4) cheap-first pattern — iterate `claudefast -p` until PASS, upgrade to one-shot `claude -p` only when stalled; (5) replacing fragile grep keyword checks with LLM-as-judge; (6) endless loop until fix on prompt / trigger wording. Trigger phrases — 验证规则进推理路径, agent-judge 循环, 便宜循环先跑跑不过再升级, claudefast -p 循环优化, probe plus judge 两档成本, LLM-as-judge 替换 grep, 规则有没有落地, 进到 CLI 推理路径, optimize prompt with claudefast, endless loop until fix, iterate until PASS REFINE FAIL. SKIP — generic refactor, code review, build fix, SQL / perf tuning, translation, summary, unrelated writing.
flow-storyboard
by liush2yuxjtuGenerate a "flow storyboard" — a single horizontal row of phone frames where every frame is a LIVE iframe of the real app, each auto-driven to a different state of the user flow (screen 1 → screen 2 → … → screen N). Each screen self-drives via a deep-link parameter (e.g. ?stage=track), so loading the board plays the whole flow at a glance, and you can redesign screen-by-screen on a living baseboard instead of static mockups. Use whenever the user wants to see a mobile/web app's whole flow side by side, asks for a "流程脚本板", "脚本板", "storyboard", "一排手机框", "把整个流程排成一排", "screen flow board", "redesign baseboard", "show every screen of the flow at once", "phone frames each driven to a state", or points at one screen/simulator and wants every key state laid out in order. Works for ANY project whose screens can be reached by a URL (deep-link param or distinct paths) — not tied to any one app. Can also publish the board as a shareable talk-html page (gist + htmlpreview preview link) via --talk mode — use that when the u
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.