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...
zjus-compose-openapi
by pirate-608Keep the ZJUers Simulator Docker backend, FastAPI OpenAPI contract, generated frontend TypeScript schemas, and thin API client in sync. Use when backend HTTP models/routes change, when src/types/api.generated.ts or src/api/client.ts may be stale, when debugging API response validation errors, or when asked to regenerate OpenAPI/types for this project.
zjus-player-onboarding
by pirate-608Work safely on the ZJUers Simulator player entry flow: invite-code auth, JWT versus persistent user token handling, character creation, initial stat budget validation, returning-user save selection, WebSocket save loading, and new-game reset behavior. Use when modifying LoginView, CharacterCreate, SaveSelect, App phase routing, auth.py, game.py, GameService, SaveService, or related tests.
zjus-pylance-noise
by pirate-608Triage and reduce Pylance/Pyright diagnostics in ZJUers Simulator. Use when Codex sees or is asked about Python/Pylance/Pyright warnings in the FastAPI backend, SQLAlchemy async models/sessions, Redis asyncio calls, OpenAI response parsing, Pydantic schemas, import resolution, venv interpreter drift, generated/cache diagnostics, or editor-only type noise that should be cleaned without hiding real defects.
code-review
by pirate-608Path-aware full-stack review workflow for ZJUers Simulator. Use when reviewing changes, running checks, triaging regressions, validating frontend/backend edits, or deciding which focused tests/type checks to run.
git-commit
by pirate-608Analyze code changes and generate professional Git Commit Messages adhering to the industry-standard Conventional Commits specification.
zjus-change-review
by pirate-608Review large ZJUers Simulator commits or working-tree changes that touch frontend and backend behavior, especially auth, character initialization, saves, WebSocket contracts, OpenAPI types, database migrations, docs, or Docker Compose. Use when the user asks for a review, regression check, or risk scan of recent changes.
zjus-docs-sync
by pirate-608Update ZJUers Simulator documentation after product, API, onboarding, deployment, architecture, or agent-workflow changes. Use when modifying VitePress pages, README files, AGENTS.md, .claude/CLAUDE.md, user guides, developer guides, navigation, screenshots/previews, API docs, setup docs, or when removing stale references such as the old entrance exam flow.
code-review
by pirate-608Path-aware full-stack review workflow for ZJUers Simulator. Use when reviewing changes, running checks, triaging regressions, validating frontend/backend edits, or deciding which focused tests/type checks to run.
code-review
by pirate-608Path-aware full-stack review workflow for ZJUers Simulator. Use when reviewing changes, running checks, triaging regressions, validating frontend/backend edits, or deciding which focused tests/type checks to run.
git-commit
by pirate-608Analyze code changes and generate professional Git Commit Messages adhering to the industry-standard Conventional Commits specification.
huixuewaiyu-readingpart
by pirate-608Automate English reading exercises on 慧学外语 (elang.zju.edu.cn). Use this skill whenever the user wants to complete reading comprehension questions on elang.zju.edu.cn, mentions "慧学外语", "elang", "英文阅读", or needs help with ZJU English reading homework. Triggers on URLs containing elang.zju.edu.cn, mentions of 慧学外语阅读, or requests to batch-complete reading exercises.
validate-and-split
by pirate-608激活虚拟环境，执行 Vashian 数据校验与拆分脚本，并核验输出。
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.