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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n9e-notify-rule-copilot
by VoyagerZHY帮助用户在夜莺(n9e)中创建、编辑、复制、排障通知规则(notify_rule)——尤其是把"P1 工作时间发钉钉+电话、非工作时间只电话""按业务组/标签路由""分级走不同通道""恢复时不打电话"这类**自然语言需求**拆成正确的 NotifyConfig 数组。当用户要求"配通知规则 / 编辑通知规则 / 调整路由 / 改适用属性 / 分级通知 / 修复匹配不上 / 拆分接收人"时使用。本技能专注**通知规则的路由层**——不动通知媒介本身（→ n9e-notify-channel-copilot），不动消息模板（→ n9e-generate-message-template），不查"为什么没发出"（→ n9e-alert-rule-troubleshoot 流程 B）。
n9e-query-alert-events
by VoyagerZHY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-query-datasource
by VoyagerZHY在夜莺(n9e)环境中查询各种数据源的数据。支持 Prometheus 指标查询、Elasticsearch/Loki 日志查询、ClickHouse/MySQL/PostgreSQL/TDengine/Doris 等 SQL 数据源查询。当用户要求查询指标、查看监控数据、搜索日志、执行 PromQL 或 SQL 查询时使用。
n9e-recommend-self-heal
by VoyagerZHY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-alert-rule-troubleshoot
by VoyagerZHYThis 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-create-alert-mute
by VoyagerZHY在夜莺(n9e)环境中创建告警屏蔽规则。当用户要求创建屏蔽规则、屏蔽告警、静默告警、添加告警抑制时使用。
n9e-create-alert-rule
by VoyagerZHY**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-alert-subscribe
by VoyagerZHY在夜莺(n9e)环境中创建告警订阅规则。当用户要求创建订阅规则、订阅告警、添加告警订阅、配置告警事件转发时使用。
n9e-create-dashboard
by VoyagerZHY在夜莺(n9e)平台上创建监控仪表盘。当用户要求创建仪表盘、监控大盘、Dashboard 时使用。
n9e-create-notify-rule
by VoyagerZHY在夜莺(n9e)环境中创建通知规则。当用户要求创建通知规则、添加通知策略、配置告警通知方式、设置通知渠道时使用。
n9e-doc-qa
by VoyagerZHYThis skill should be used when the user asks "how-to" or factual questions about the 夜莺(n9e) / Flashcat platform — 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 VoyagerZHY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。
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.