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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promql-generator
by ccfos根据自然语言生成 PromQL 查询语句
ops-troubleshooting
by ccfosThis skill should be used when the user asks to "troubleshoot", "diagnose", "debug alert", "investigate incident", "故障定位", "告警排查", "问题诊断", "排障", "查告警", "分析告警", "根因分析", "查指标", "查日志", or discusses monitoring/alerting/observability issues in 夜莺(n9e) platform.
n9e-alert-rule-troubleshoot
by ccfosThis skill should be used when the user reports that an alert rule is "not firing", "没发告警", "告警不触发", "规则没生效", "应该报警但没报警", "为什么没收到告警", "alert rule not firing", or wants to diagnose why a specific alert rule failed to produce an event/notification. 适用于排查"告警规则为什么没正常发出告警"，而不是看已有告警找根因（后者用 ops-troubleshooting）。仅支持 Release 22 及以上版本。
n9e-import-prom-rule
by ccfos**批量导入 Prometheus 告警规则 YAML 文件**（一次性建一组规则）。专用于处理远端 URL 或本地 YAML 文本，自动解析 `groups` / 纯 `rules` 数组 / 单条 rule 三种格式。 ⚠️ **不要用这个 skill 做单条创建**——用户用自然语言描述一条告警需求时，请改用 n9e-create-alert-rule。 触发：导入 / import / 批量 / URL / .yml 文件 / .yaml 文件 / awesome-prometheus-alerts / node-exporter.yml / prometheus rule file。
n9e-query-alert-events
by ccfos在夜莺(n9e)环境中查询告警事件。当用户要求查看告警、查询活跃告警、搜索历史告警、查看告警详情、统计告警事件时使用。
n9e-recommend-self-heal
by ccfos为已触发的告警事件推荐自愈动作（半自愈 / auto-heal recommendation）。当用户从告警事件详情页或通知卡片打开 Copilot 问"这条告警能自愈吗"、"推荐个自愈脚本"、"帮我处理一下"、"一键修复"时使用。本技能只做**推荐**——不执行；执行走前端按钮调 ibex 接口。需要 context.event_id。
n9e-analyze-dashboard
by ccfos分析夜莺(n9e)上某个仪表盘在一段时间内的数据健康状况。当用户要求"分析某仪表盘有什么问题"、"看看 xx 大盘最近 24 小时正不正常"、"巡检这个大盘"、"这个 dashboard 有没有异常"时使用。区别于修改仪表盘(n9e-modify-dashboard)和创建仪表盘(n9e-create-dashboard)。
n9e-create-alert-rule
by ccfos**创建告警规则**。优先复用 integrations 里验证过的规则（标准组件 Linux/MySQL/Redis/Kafka/PostgreSQL/Elasticsearch 等都有现成规则包），导入几条按用户需求来——单条、一批、或整套都行；integration 里没有贴合的规则时再手写自定义规则。支持 Prometheus / Loki / ES / OpenSearch / MySQL / PG / TDengine / ClickHouse / Doris / VictoriaLogs / Host 全部数据源。 ⚠️ **不要用这个 skill 做批量 YAML 导入**——用户给的是 URL 或 YAML 文件、awesome-prometheus-alerts、node-exporter.yml 之类，请改用 n9e-import-prom-rule。 触发：创建一条/加一条告警 / 帮我建个 CPU 告警 / 给 MySQL 加套告警规则 / 给主机配上常用告警 / 我要监控某个指标。
n9e-create-dashboard
by ccfos创建监控仪表盘。当用户要求创建仪表盘、监控大盘、Dashboard 时使用。
n9e-doc-qa
by ccfosThis skill should be used when the user asks "how-to" or factual questions about the 夜莺(n9e) — UI/where-to-click, 业务组/订阅规则/屏蔽规则/edge 模式, Token 使用, 通知 pipeline, 自愈触发条件; OR about categraf input plugin field meanings, metric names, defaults, environment variables, config syntax (e.g. "[[instances]] 怎么写", "ping_average_response_ms 单位"). NOT for actively troubleshooting an alert or querying metrics.
n9e-generate-message-template
by ccfos生成或修改夜莺(n9e)告警通知消息模板。当用户要求写通知模板、改消息格式、加主机名/恢复值/级别、钉钉/飞书/Lark/邮件/短信/电话模板时使用。
n9e-host-health-diagnose
by ccfos帮用户判断一台机器到底是 真宕机 / agent 假死 / 网络抖动 / 维护中。当用户问"为什么这台机器失联"、"host 失联告警是不是误报"、"categraf 卡住了吗"、"心跳停了为啥还能 ping 通"等触发本技能。核心立场：**agent 失联 ≠ 主机宕机**。只看 target_up==0 / BeatTime 停就下"宕机"结论，是常见的误报根源。
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.