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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security-vulnerability-fix
by Shen-Ming-Hong修復 npm 依賴安全漏洞的完整工作流程。當使用者提到安全警告、Dependabot alerts、CVE 漏洞、npm audit 問題時自動啟用。包含查詢漏洞、升級依賴、驗證修復、發布版本的完整流程。Fixes npm dependency security vulnerabilities with a complete workflow including Dependabot alert analysis, package upgrades, build verification, version release following semantic versioning.
security-checker
by Shen-Ming-Hong編輯程式碼時的安全檢查技能。當使用者編輯檔案、修改程式碼、或進行 code review 時自動啟用。監控命令注入、XSS、eval 使用、敏感資料外洩、不安全的 postMessage 等安全風險。靈感來源於 Anthropic 官方 security-guidance plugin。Security checking skill that monitors for potential security issues when editing code, including command injection, XSS, eval usage, credential exposure, and unsafe postMessage patterns. Inspired by Anthropic's official security-guidance plugin.
pr-review-release
by Shen-Ming-HongPR Code Review 評估與完整發布流程。當使用者提到 code review、PR 審查、review 建議處理、merge PR、發布版本、release、squash merge、版本標籤時自動啟用。包含評估 Copilot/人工 review 建議、程式碼修正、Git 合併、語意化版本更新、CHANGELOG、打包發布的完整工作流程。PR review evaluation and release workflow for processing code review comments, merging PRs, semantic versioning, and publishing releases.
git-workflow
by Shen-Ming-HongGit 工作流程自動化技能，涵蓋從 commit 到發布的完整流程。當使用者提到 commit、push、建立 PR、pull request、提交程式碼、推送分支時自動啟用。包含自動生成 Conventional Commits 格式訊息、建立 PR、等待 Code Review、觸發發布流程等功能。Automates Git workflow from commit to release. Inspired by Anthropic's official commit-commands plugin.
add-cyberbrick-sample
by Shen-Ming-Hong新增或更新 CyberBrick MicroPython 範例工作區的完整工作流程。當使用者提到新增範例、更新範例、add sample、update sample、修改範例積木、建立範例積木、新增 CyberBrick 示範程式、sample workspace、範例工作區、更新翻譯、stringTranslations、nameTranslations、字串翻譯、標籤翻譯 時自動啟用。包含從 Blockly 工作區匯出 JSON、建立或覆蓋範例檔、更新索引、15 語系翻譯填寫（含識別字名稱翻譯 nameTranslations 與 text 積木字串翻譯 stringTranslations）、本機驗證到推送上線的完整流程。Full workflow for adding or updating a CyberBrick MicroPython sample workspace: export Blockly JSON, create or overwrite sample file, update index, fill 15-language translations (nameTranslations for identifiers, stringTranslations for text labels), local validation, and push to production.
code-simplifier
by Shen-Ming-Hong程式碼簡化與重構工作流程。當使用者提到簡化程式碼、清理 PR、重構、程式碼優化、code cleanup、refactor、simplify code、clean up complex code 時自動啟用。靈感來源於 Anthropic Claude Code 團隊內部使用的 code-simplifier agent。A code simplification and refactoring workflow. Inspired by the official code-simplifier agent used internally by the Claude Code team at Anthropic.
txt-hardware-debug
by Shen-Ming-Hongfischertechnik TXT Controller 硬體 SSH 除錯技能。當使用者提到 TXT 控制器、ftrobopy、 超音波感測器、TXT SSH 除錯、io_server.py 錯誤、TXT 硬體測試、TXT sensor API、 txt hardware debug、ftrobopy API、TXT connection、TXT sensor reading、 TXT ultrasonic distance、txt.input().state()、txt.ultrasonic().distance() 時自動啟用。涵蓋 SSH 連線、ftrobopy API 探索、感測器讀值方法確認、io_server.py 部署 與測試的完整除錯流程。
specs-consolidation
by Shen-Ming-Hong整合 specs/ 資料夾的規格文件到 docs/specifications/ 的完整工作流程。 當使用者提到整合規格、清理 specs、merge specs、整理技術規格、specs 到 docs、 consolidate specs、spec cleanup、規格整合、歸檔規格文件時自動啟用。 依照規格編號由小到大依序整併，以較新的規格內容為準，刪除已整合的舊規格目錄， 僅保留最新一筆 specs。 Consolidates specs/ documents into docs/specifications/ in chronological number order. Newer spec content overrides older when overlapping. Deletes merged spec folders, keeping only the latest spec.
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.