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...
writing-style-skill
by jzOcb可复用的写作风格 Skill 模板。内置自动学习： 从你的修改中自动提取规则，SKILL.md 越用越准。 Fork 后改成你自己的风格。
context-doctor
by jzOcbVisualize and diagnose OpenClaw context window usage. Generates a terminal-rendered breakdown showing workspace files (status, chars, tokens), installed skills inventory, and token budget allocation across bootstrap components. Use when: (1) user asks about context window health or token usage, (2) debugging agent quality degradation ("agent got dumber"), (3) after editing workspace files to verify impact, (4) auditing bootstrap overhead. NOT for: conversation history analysis, model selection, or cost tracking.
memory-management
by jzOcbManage AI agent memory with P0/P1/P2 priority system and auto-archival. Use when: setting up memory management, cleaning up MEMORY.md, creating lessons files, configuring auto-archive cron, or reviewing memory structure. Reduces token usage by 70-80% while maintaining recall via semantic search.
agent-guardrails
by jzOcbStop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.
upgrade-guard
by jzOcbSafe OpenClaw upgrades with snapshot, pre-flight checks, controlled upgrade steps, post-verification, and emergency rollback. Never lose your working system to a bad update again.
handoff-openclaw
by jzOcbOpenClaw 专用的任务交接 skill：生成标准化 handoff 文档并交给 sub-agent 或发送到指定 topic。 Use when 用户说“handoff/交接/传给 codex/让 sonnet 做/拆分任务/spawn 执行”。 Includes OpenClaw actions: sessions_spawn, message(send), topic 发送。
handoff-claude
by jzOcbClaude 通用版任务交接 skill：生成清晰、可执行的 handoff 文档，适配 Claude.ai/Claude Code。 Use when 用户说“handoff/交接/继续上一个任务/把研究交给执行”。 Tool-agnostic: 不依赖 OpenClaw 专有工具。
config-guard
by jzOcbPrevent OpenClaw config changes from crashing the gateway. Auto-backup, schema validation, critical field checks, and auto-rollback. Use before any config.apply, config.patch, or openclaw.json edit.
token-guard
by jzOcbMonitor and control OpenClaw token usage and costs. Set daily budgets, track spending, auto-downgrade models when limits hit. Stop burning money while you sleep.
process-guardian
by jzOcbManage long-running background processes (bots, scrapers, monitors, servers) with reliable detached execution, automatic health monitoring, auto-restart on failure, and proactive alerts. Use when launching any process that should survive session disconnects, when checking process health, or when a background task keeps dying silently.
infra-guardian
by jzOcbOpenClaw Agent Infrastructure Guardian — keep your agent's infrastructure alive. Process lifecycle management with detached execution, auto-restart on failure. Cron scheduler health monitoring (per-job detection, auto-recovery). Direct Telegram/messaging alerts independent of OpenClaw. System-level watchdog that runs from crontab, not OpenClaw cron. Use when launching background processes, monitoring cron job health, or when things keep dying silently.
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.