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.
Querying local SQLite index...
github-pr-assets
by zly2006Manage screenshots and other visual assets for GitHub pull requests. Use when a PR body needs real screenshots, when local /tmp paths must be replaced with durable links, when deciding whether GitHub web drag-and-drop uploads can be reproduced with gh api, or when updating a PR without disturbing the user's browser tabs.
zhihu-reproduce
by zly2006当用户要求复刻知乎网页版的功能、交互、接口行为或视觉细节时，使用此技能采集真实网页证据、分析 API 与 UI 结构，并落地到 Zhihu++ Android 代码。
launch-on-device
by zly2006Build, install, and launch the Zhihu++ Android app on a connected device using ADB. Includes comprehensive troubleshooting for common issues like missing devices, installation failures, signature mismatches, and app crashes. Use when deploying debug builds to physical devices or emulators.
ui-voyager
by zly2006以 subagent 启动的 UI 漫游与异常发现技能。Use when the user, AGENTS, or the current task requires “UI漫游者”. 该 skill 负责系统性地把目标页面能点的都点一遍、把上下左右能滑的都滑一遍，重点发现空白页、越界、裁切、错位、布局失衡、状态切换异常等问题；必要时结合 ui-test 与截图检查，并把意见记入 .memory/YYYY-MM-DD/ui-volayor。
ui-test
by zly2006Zhihu++ LLM 自动化 UI 测试。使用 testTag 系统精准定位 Compose 元素并交互，替代硬编码坐标的 adb tap。提供已知 tag 列表、文字内容点击、截图验证等能力。适用于：功能验证、UI 回归测试、自动化交互流程。
picky-user
by zly2006以 subagent 启动的 UI 挑剔用户评审。Use when the user, AGENTS, or the current task requires “挑剔的用户”. 该 skill 会分别扮演新用户与老用户，对界面的 self explain、明确性、直觉性、效率、布局和操作习惯提出高标准意见；默认给出 5-10 条有效建议，必要时结合 ui-test 与截图检查，并把意见记入 .memory/YYYY-MM-DD/picky-user。
release-latex-fork
by zly2006Release the local LaTeX fork used by Zhihu++. Use when you need to merge upstream huarangmeng/latex into the zly2006 fork, preserve only approved fork deltas (font removal), publish a new Maven Central release, update Markdown's latex dependency, and verify the build. Applies specifically to /Users/zhaoliyan/IdeaProjects/latex as the fork checkout.
release-markdown-fork
by zly2006Release the local Markdown fork used by Zhihu++. Use when you need to merge upstream huarangmeng/Markdown into the zly2006 fork master, preserve only approved fork deltas, publish a new Maven Central alpha, update Zhihu's Android dependency versions, and verify the build. Applies specifically to /Users/zhaoliyan/IdeaProjects/Zhihu and its .tmp/Markdown-zly2006 fork checkout.
zhihu-pp-ai-slop-cleaner
by zly2006Use for Zhihu++ maintenance work that scans Kotlin main sources for low-call functions, structurally similar function bodies, dead code, pure forwarding wrappers, pointless abstractions, repeated helpers, and cross-platform duplicate glue before refactoring or PR cleanup. Trigger when the user asks to clean AI slop, remove low-call wrappers, find similar or duplicated code, review duplicated helper functions, or audit functions with call count at most 2 in /Users/zhaoliyan/IdeaProjects/Zhihu.
zhihu-parallel-pr-workflow
by zly2006Coordinate Zhihu++ issue and PR implementation through subagents with isolated git worktrees, off-host Android AVD validation, real screenshots, Chinese PR creation, and main-agent-only review/coordination. Use when the user asks to fan out issues, maximize parallel subagent work, implement multiple Zhihu++ features or fixes, validate on `$off-android-avd-ci-debug`, or automatically open PRs from worker branches.
mixin-injection-point-discovery
by zly2006Locate robust Mixin injection points from unobfuscated Minecraft jars by reading bytecode signatures with javap and validating method ownership, descriptors, and event semantics. Use when implementing or fixing Fabric mixins, especially in 26.1 versions where mappings may differ.
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.