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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observer-log-analysis
by oceanbase标准 SOP：OceanBase 集群侧（observer/election/rootservice）日志采集、过滤与 analyze 解读。用户要收集日志、trace_id/关键词过滤、分析错误栈或慢 SQL 在 observer 日志中的线索时使用。OBProxy 日志请用 obproxy-log-analysis。
observer-sql-analysis
by oceanbase标准 SOP：单条 SQL 性能、执行计划、trace 计划包、锁等待。用户问「为什么慢」、提供 trace_id、或描述锁/超时时使用；与 obdiag-performance（整体 ASH/AWR）、observer-log-analysis（observer 日志链）分工。
observer-storage-space-troubleshooting
by oceanbaseObserver 侧日志盘、数据盘、磁盘分配失败、-4184、-4264、-4009、Structure needs cleaning、fallocate、合并 -4016、索引或加副本空间不足、单表恢复占盘、归档卡 GC 等存储空间问题；日志链使用 observer-log-analysis。
oceanbase-knowledge
by oceanbase官方 OceanBase 知识库（文档/参数/概念）。用户问手册级问题、参数含义、架构与运维说明时使用；不用于查询实时集群状态。需在 agent.yml 中开启 oceanbase_knowledge.enabled 并配置 bearer_token。网关 POST /retrieval；component 仅允许固定枚举（见正文，默认 oceanbase）。
oceanbase-source-analysis
by oceanbaseOceanBase 社区版源码分析 SOP。当日志/RCA/巡检无法给出确定性结论时，通过读取本地 OceanBase 社区版源码进行精准验证。覆盖错误码溯源、函数/参数行为确认、调用链追踪、跨版本行为对比。需用户提供本地源码路径（源码约 3-5 GB）。
oms-kafka-performance
by oceanbase标准 SOP：OceanBase 社区版经 OMS 同步至 Kafka 的延迟、吞吐与调优。涵盖全量/增量并发与限速、KafkaSink、OMS 平台 limitator 与 JVM、源端 OB 与表结构策略；与 observer 日志、OBProxy、单条 SQL 深查 skill 分工明确。
obproxy-routing-troubleshooting
by oceanbaseOBProxy/ODP 连接解析、弱读与只读副本路由、读写分离、分区键解析、SHOW CREATE 兼容、get_lock、cluster not exist、2013、Unknown thread id、新建表经代理慢等路由配置类问题；日志采集解读使用 obproxy-log-analysis。
obdiag-rca
by oceanbaseOceanBase 集群所有根因分析场景的统一 SOP。覆盖事务超时/回滚/断连、内存不足、合并卡住、日志盘满、DDL 失败、Schema 泄漏、GC 异常、弱一致性读、OMS 等。始终先执行 rca_run，如 RCA 脚本无法给出结论再按本 skill 降级到手动日志采集分析。
obdiag-triage
by oceanbase未知/未分类问题的统一分诊 SOP：用户描述集群异常但症状不明确、不知从何入手，或问题无法直接对应 rca/observer-log-analysis/observer-sql-analysis 等专项 skill 时，强制先执行全量巡检 check_cluster，再根据巡检结果决定下一步路径。
obdiag-usage
by oceanbaseobdiag agent 工具使用指南：配置文件生成、gather/analyze/check/rca 各命令的调用方式、参数说明与典型场景。用户询问"怎么用 obdiag"、"如何采集日志"、"怎么生成配置"、"如何巡检/根因分析"，或 agent 自身不确定该调哪个工具时加载本 skill。
obproxy-log-analysis
by oceanbase标准 SOP：OBProxy 日志采集（gather_obproxy_log）、包内解读（file_list/file_read/run_shell 解压）、可选巡检。用户提到 OBProxy/代理/obproxy_diagnosis 等时使用；与 observer 侧 observer-log-analysis 互斥 analyze_log。
backup-archive-restore-troubleshooting
by oceanbase备份、日志归档、物理恢复、恢复时间窗、NFS/OSS/COS 归档路径、对象存储权限、备租户恢复源、ob_admin 读取归档位点、oblogminer schema_meta 等问题；OMS 迁移同步问题不使用本 skill。
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.