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...
bug-hunter
by DragonOS-Community分布式多智能体缺陷检测总控技能。基于输入随机化、角色化并行评审、语义桶化、加权共识与裁决复核输出高信噪比代码评审报告。用于大规模 PR、复杂逻辑变更、安全敏感改动或单智能体评审召回率不足的场景。
bug-hunter-stage1-input-randomization
by DragonOS-Communitybug-hunter 阶段 1 技能。负责提取代码改动、执行敏感信息脱敏，并按文件/代码块生成多轮随机化输入以缓解 LLM 位置偏差。
bug-hunter-stage2-parallel-review
by DragonOS-Communitybug-hunter 阶段 2 技能。负责将随机化后的 diff 按 persona 矩阵分发给 8 个子智能体并行评审，并收集统一 JSON 结果。
bug-hunter-stage3-evidence-fusion
by DragonOS-Communitybug-hunter 阶段 3 技能。负责对多智能体原始发现做语义去重、桶化聚类与冲突识别，形成可投票的缺陷候选池。
bug-hunter-stage4-consensus-judge
by DragonOS-Communitybug-hunter 阶段 4 技能。负责对缺陷桶执行加权共识投票，筛选过阈值问题，并输出裁决级结构化评审报告。
dragonos-develop-nix-yolo-boot-check
by DragonOS-Community专用于按照 docs/introduction/develop_nix.md 的流程，通过 Nix dev shell / yolo 命令启动 DragonOS，并在 QEMU nographic 串口中做启动烟雾检查或实时轮询回贴输出。当用户要求“按 develop_nix 跑 yolo”“用 nix yolo 启动 QEMU 看输出”“边跑边轮询输出”“进 guest 后检查 /proc、/sys/fs/cgroup、mount 是否正常”时使用。
dragonos-gvisor-test-analysis
by DragonOS-Community通过对比 Linux/gvisor 参考实现来分析 DragonOS gVisor 测试失败。输出结构化的修复文档，包含对于所有失败用例的表格格式分析文档，针对每个具体的失败用例的详细格式修复文档，并提供代码片段。当用户提及 gVisor 测试失败、特定测试用例或询问 bug 分析/修复方案时使用。
dragonos-atomic-snapshot-debug
by DragonOS-Community使用低扰动原子快照、GDB 现场采样和语义对比来调试 DragonOS 内核中的时序问题、Heisenbug、阻塞挂起、丢唤醒和“加日志现象改变”的问题。适用于网络、VFS、调度、IPC、驱动等子系统；当用户提到任务卡住、CPU idle 但请求不返回、阻塞点偶发失效，或明确要求在线取证且不想依赖高频日志时使用。
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.