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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oh-capi-xts-gen
by openharmonyinsightOpenHarmony CAPI N-API 封装测试用例生成器（方式2）。自动解析 .h 头文件，生成 C++ N-API 封装和 ETS/ArkTS 测试代码。 **触发此技能的场景**： - 用户提到 CAPI、C 测试、Native 测试、原生测试、.h 文件解析 - 用户需要生成 N-API 封装测试（不是 gtest） - 用户提到覆盖率报告、补充测试、缺失测试 - 用户需要编译或验证 CAPI 测试套 - 用户提到异步编译、后台编译、编译验证 - 用户提到 XTS 测试、OpenHarmony 测试、子系统测试 **核心功能**： 1. 头文件解析 → 提取 API 信息 2. 测试用例生成 → C++ N-API 封装 + ETS 测试代码 3. 编译验证 → 异步编译 + 错误修复 4. 代码验证 → N-API 三重校验 + 格式检查 **支持子系统**：multimedia、bundlemanager、ability、arkui、hilog 等 **当用户提供覆盖率报告时**：自动切换到覆盖率报告驱动模式（高效精准）
arkweb-thread-safety-review
by openharmonyinsightMust use when scanning or reviewing OpenHarmony ArkWeb/Chromium C++ single files for thread-safety violations involving PostTask, BindOnce, base::Unretained(this), WeakPtr, scoped_refptr, mojo::Connector, GPU/Audio/DrDC threads, NDK/UI callbacks, NWeb, WebContents, Profile, BrowserContext, NavigationController. Enforces false-positive control and outputs strict JSON schema violations for standard_rule1-standard_rule10 only.
hmos-multidevice-screen-window-size
by openharmonyinsightHandle HarmonyOS screen and window size adaptation, including breakpoint systems, responsive layouts, GridRow/GridCol usage, window size observation, and multi-device layout changes.
oh-pdd-design-doc-generator
by openharmonyinsight基于 PRD 文档自动生成鸿蒙系统设计文档，包括架构设计文档和功能设计文档。生成前会分析 OpenHarmony 存量代码结构，确保与现有架构兼容。架构设计文档第2章必须为竞品方案分析，位于需求背景之后。适用于用户请求：(1) 生成架构设计文档, (2) 生成功能设计文档, (3) 从 PRD 生成设计文档, (4) 创建系统架构设计, (5) 编写功能规格说明, (6) 分析 OH 代码结构。关键词：architecture design, functional design, design doc, 竞品方案分析, OpenHarmony code analysis, 架构设计, 功能设计, 设计文档生成, OH代码分析, analyze codebase, competitor analysis
oh-pdd-prd-analysis
by openharmonyinsight分析 HM Desktop PRD 文档，提取需求信息、验证完整性、检查章节顺序（需求来源→需求背景→需求价值分析→竞品分析→需求描述）、检查 KEP 定义、检测需求冲突并生成结构化分析报告。适用于用户请求：(1) 分析或审查 PRD 文档, (2) 从需求中提取 KEP 列表, (3) 检查 PRD 完整性或一致性, (4) 将需求映射到模块架构, (5) 验证 PRD 格式合规性, (6) 验证竞品分析章节完整性。关键词：PRD分析, requirement extraction, KEP验证, completeness check, chapter order validation, 竞品分析检查, analyze PRD, 需求提取, 完整性检查, 章节顺序验证
oh-pr-workflow
by openharmonyinsightOpenHarmony PR full lifecycle workflow. Five modes: - Commit: standardized commit with DCO sign-off and Issue linking - Create PR: commit + push to fork + create Issue + create PR on upstream - Fix Codecheck: fetch gate CI codecheck defects from a PR and auto-fix them - Review PR: fetch a PR's changes to local for code review - Fix Review: fetch unresolved review comments from a PR and auto-fix them Triggers on: /oh-pr-workflow, "提交代码", "创建PR", "提个PR", "commit", "修复告警", "修复门禁", "修复codecheck", "fix codecheck", "review pr", "review这个pr", "看下这个pr", "检视pr", "修复review", "修复检视意见", "fix review", or a GitCode PR URL with fix/review intent.
oh-precommit-codecheck
by openharmonyinsightRun local code quality checks covering a subset of OpenHarmony gate CI (copyright, CodeArts C/C++) plus additional local checks (pylint/flake8, shellcheck/bashate, gn format). Use before committing to reduce gate failures. Triggers on: /oh-precommit-codecheck, "门禁检查", "门禁预检", "检查代码", "run codecheck", "check code quality", "lint my code", "代码检查", or after completing code implementation. WHEN to use: before git commit, before creating PR, after modifying C/C++/Python/Shell/GN files, when gate CI fails with codecheck defects, or when you want to preview what gate will flag.
oh-xts-generator-template
by openharmonyinsightOpenHarmony XTS 测试用例通用生成模板。支持各子系统测试用例生成，API 定义解析，测试覆盖率分析，代码规范检查。触发关键词：XTS、测试生成、用例生成、测试用例。
oh-arkruntime-thread-safety-audit
by openharmonyinsightUse this skill when auditing, reviewing, or fixing thread-safety issues in ArkCompiler Runtime Core, especially ArkTS-Sta ETS stdlib and plugin code under static_core/plugins/ets. It covers static mutable state, singleton initialization, shared maps/caches/counters/timers, taskpool/EAWorker concurrency, TSAN follow-up, and concurrent test design.
oh-distributed-security-design-review
by openharmonyinsightOpenHarmony分布式系统安全代码检视专用技能。当用户要求"检视代码安全实现"、"代码安全审查"、"安全代码review"或类似的分布式系统代码安全检视请求时触发。此技能提供18条OpenHarmony分布式业务安全设计规则的详细检视指导，涵盖授权控制、状态机、数据传输、权限管理、可信关系等安全领域。使用此技能可在通用网络安全规则基础上，针对OpenHarmony分布式系统进行专项安全检视。
oh-graphic-gitcode-pr-creator
by openharmonyinsightAutomate Gitcode Pull Request creation workflow for OpenHarmony graphic subsystem including code commit, issue creation, and PR submission with proper template formatting. Use when user needs to create a PR for the Gitcode platform, especially for OpenHarmony graphic projects that require specific PR templates with CodeCheck tables and Signed-off-by tags.
oh-interfaces-ipc-to-service
by openharmonyinsight在Openharmony项目的graphic_2d仓新增透传通路。当用户希望在graphic_2d仓新增RSInterface接口，且指明该接口为ToSerivce时，调用该能力
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.