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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pre-commit-review
by eclipse-oniro-mirrorsPre-commit code review for OpenHarmony projects based on C++ coding standards. Use when reviewing code before commit, checking local changes, or verifying OpenHarmony style compliance. Triggers on pre-commit review requests, git diff/staged changes inspection, and code quality checks.
gitlog
by eclipse-oniro-mirrorsGit log skill for analyzing commit history, generating git status reports, and working with git repositories. Use this when users need to view commit history, check repository status, analyze changes, or generate git-related reports.
ohproj
by eclipse-oniro-mirrors创建、编译、签名、编译测试、执行测试 OpenHarmony 原生应用项目（基于 NativeProj46R 模板），含 NAPI 对接规范与测试报告说明。可选关联 ohservices 技能与 ohsa.py：在同仓库做系统镜像/SystemAbility 开发或需设备侧 hilog/hidumper 诊断时使用。
rkflash
by eclipse-oniro-mirrorsRockchip OH 镜像技能：同步须配置 rkflash_sync_config.json 或 RKFLASH_* 环境变量（无内置主机/密码）；pscp/scp/paramiko 同步；config.cfg 驱动烧录；默认版本校验与 VERIFY OK 后关灯。见 SKILL.md。
thread-deadlock
by eclipse-oniro-mirrorsPrevent deadlock in multithreaded code - enforce lock ordering, timeout strategies, and deadlock detection patterns
ohppt
by eclipse-oniro-mirrors将 Markdown 文档中的表格转为结构图 PPTX（每行一框、列内子模块、支持层级分隔）。依赖 python-pptx。脚本 ohppt.py、build_architecture_ppt.py。
ohhdf
by eclipse-oniro-mirrorsOpenHarmony HDF on rk3568: Light + bluehdf architecture, HCS/UHDF/HAL/KHDF, compile/push/flash, client C API counts, hdc hilog dmesg hidumper debugging. Scripts: ohhdf.py; docs: howtohdf.md, hdf_guide_zh.md.
bms-testing-patterns
by eclipse-oniro-mirrors用于为 OpenHarmony BundleManager 添加、定位或调整测试。 当需求涉及单元测试、系统测试、模块测试、benchmark、fuzz、GN 测试 target、mock、测试 HAP/resource、安装流程测试、IPC 测试、权限/校验测试或回归测试设计时使用。
ohhap
by eclipse-oniro-mirrorsOpenHarmony HAP 应用构建与签名：环境检查、SDK 版本校验、hvigor 编译主包与 ohosTest；在设置 OHOS_HAPSIGNER_RESULT 时 build/build-test 成功后自动命令行签名，亦可单独 sign 或清除签名。需 HOS_CLT_PATH、OHOS_SDK_PATH 与项目 build-profile.json5。脚本 hapbuild.py。与 ohbuild（fuzz/部件编译）不同。
doc
by eclipse-oniro-mirrors总结文档 skill。在所有任务执行完毕后，汇总整个需求实现过程的信息，产出完整的功能总结文档和测试报告。与工作流无关，由调用方传入工作目录和上下文。
review
by eclipse-oniro-mirrors代码检视 skill。独立于执行阶段对代码变更进行检视，从代码质量、变更范围、接口兼容性等维度进行静态检视，并提供详细的检视意见。与工作流无关，由调用方传入工作目录和上下文。
unit-test-generator
by eclipse-oniro-mirrorsGenerate comprehensive unit tests for OpenHarmony Distributed Notification Service that follow project standards include proper assertions use appropriate mocks and achieve 90% branch coverage. Use when adding tests for new code commits files or improving coverage.
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.