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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ui-e2e
by openJiuwen-ai运行 JiuwenSwarm Web UI 端到端测试并收集截图、日志、report.md、report.json。用于验证 Todo 和 Cron Web UI 流程、复现浏览器交互问题、选择运行解释器、准备 Playwright 环境,或返回可操作的失败证据时。
llm-wiki
by openJiuwen-aiBuild and maintain a persistent knowledge base (LLM Wiki) using native backend tools. Supports ingesting PDFs, Markdown, and TXT files, and querying or linting the data. Use this whenever the user wants to add, retrieve, or manage their own local knowledge base and documents.
financial-document-parser
by openJiuwen-aiExtract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
advanced-daily-report
by openJiuwen-ai进阶版日报生成器,支持多数据源采集、工作分析、趋势对比、周报月报聚合
akg-agents
by openJiuwen-ai代理执行 AKG 算子任务。先检查固定仓库与分支;若 `~/.akg/check_env.md` 不存在则强制 `FULL_SETUP=true`;忽略所有 `akg_cli` 检查和使用;后端代码生成直接执行 `run_workflow.py --workflow kernelgen`。
ascend-moe-optimizer-auto-trace
by openJiuwen-ai为昇腾算子在源码中接入 TRACE_POINT 与 MoeTracing,串通 trace_preprocessor、profiling tensor、point_map.json、 save_profiling_data 与 trace_collector 生成 Chrome trace。强调门禁 G1–G5:全链路预处理与 OPP、profiling 为数据输出最后一位、 整条编译与示例脚本联调、落盘路径在 spawn 前 resolve。遵循函数级粒度与就地扩展,禁止另注册 xxx_profiling 类第二入口, 保持原 Op 与 torch.ops 名称及签名不变。在用户提到算子打点、Profiling、Chrome trace、MoeTracing,或将结论写入本 skill 时读取。
swarmskill-creator
by openJiuwen-aiCreates, converts, or modifies Swarm Skills — the multi-role (多角色团队) extension of the Skills standard, optionally with an executable SwarmFlow orchestration script. Use when building or refactoring a multi-agent team, generating workflow (工作流/编排) orchestration code, or upgrading a single-agent skill into a collaborating team. Do NOT use for ordinary single-agent skills — use create-skill instead.
skvm-jit
by openJiuwen-aiTrigger `skvm jit-optimize` with post-task evidence so it generates a reviewable optimization proposal for a skill. Use after finishing a task that was driven by any skill (not just SkVM-compiled ones) when the outcome was a failure, was partial, or the skill's instructions clearly caused confusion or an unnecessary detour. Clean successful runs usually do not need an optimization pass. For general skvm usage (profile/aot-compile/bench/proposals), use the sibling `skvm-general` skill instead.
skvm-general
by openJiuwen-aiDrive the skvm CLI on behalf of a user to profile models, AOT-compile skills, run skill-assisted tasks, run benchmarks, and manage compiled proposals. Trigger when the user asks to "profile", "aot-compile", "bench", "run a single ad-hoc task with a skill", or asks about skvm proposals. Do NOT trigger for `jit-optimize` or when the user wants to optimize/improve a skill — use the sibling `skvm-jit` skill instead.
ascend-moe-optimizer-trace-analyzer
by openJiuwen-ai在用户提供 Chrome/Perfetto trace.json、或排查 Ascend 上 MoE/FusedDeepMoe 等算子性能时使用。按 phase、category、core group、tid 统计耗时、overlap、bubble,输出 CSV、Markdown 报告与确定性诊断;可选外部 LLM 扩写分析。默认 phase 映射面向 UMDK FusedDeepMoe,其它 trace 需替换或扩展 config/phase_map.yaml。
cross-channel-history-retrieval
by openJiuwen-ai跨会话检索聊天原文(记忆不足时再用)。在回答任何关于历史事件、日期、人物、过去对话的问题时,如果记忆中没有相关信息或不足以回答,则需要使用跨会话检索聊天原文。用 mcp_exec_command 执行 scripts/search_history.py,读 ~/.jiuwenswarm/agent/sessions/*/history.json。支持 channel、session_id、关键词、时间窗。如果搜索结果不足,尝试用不同的关键词再次搜索。
delayed-restart-app
by openJiuwen-ai安排延迟重启本 Agent 所在的服务(JiuwenSwarm app)。执行后当前 Agent 进程会被终止并重新启动,当前会话会断开。用于用户要求重启、配置更新需生效、或服务异常需重载时。使用 bash 调用脚本。
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.