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...
taiwanmd-validate
by frank890417Validate Taiwan.md articles for editorial quality, frontmatter correctness, and content standards. Use when reviewing PRs that touch knowledge/ or src/content/, writing new articles, or when the user asks to check article quality. Runs frontmatter validation, word count check, and reports issues. TRIGGER when: user says "validate", "check article", "review quality", or when editing .md files in knowledge/ or src/content/.
twmd-diary
by frank890417Write Taiwan.md diary entry via canonical DIARY-PIPELINE. TRIGGER when: user says "寫日記", "反芻", "Beat 5", "DIARY".
twmd-memory
by frank890417Write Taiwan.md session memory via canonical MEMORY-PIPELINE. TRIGGER when: user says "收官", "寫 memory", "Beat 4", "session 收官".
twmd-music-media-audit
by frank890417Music / 音樂類 People / 演員 / 運動員 條目 iframe 缺口 audit per EDITORIAL §媒體編織 baseline. 產出 reports/music-media-audit/YYYY-MM-DD.md heal candidate list. TRIGGER when: user says "音樂 audit", "缺影片", "music media audit", "iframe 盤點".
twmd-routine
by frank890417Routine 飛輪管理 — 讀 ROUTINE.md SSOT 後做對應改動或新增 routine 任務 (cadence / skill / quality gate / escalation / mirror sync). TRIGGER when: user says "排 routine", "改 routine", "新增 routine", "調整 cron", "更新 quality gate", "routine 飛輪", "routine SSOT".
twmd-bench
by frank890417Sovereignty-Bench-TW measurement via canonical BENCH-PIPELINE. TRIGGER when: user says "跑 bench", "Sovereignty-Bench", "加新 model", "bench-pipeline".
twmd-spore-publish
by frank890417Daily auto-publish spore from SPORE-INBOX via canonical SPORE-PUBLISH-PIPELINE — pick high-quality entry → 4 hard gate → ship Threads + X → 復盤. Routine fires 10:00 daily; manual via "/twmd-spore-publish" or "發今天的孢子" or "跑 spore publish". TRIGGER when: routine twmd-spore-publish-daily fires / user says "跑 spore publish" / "發今天的孢子" / "從 SPORE-INBOX 選一篇 ship".
twmd-babel
by frank890417Multi-language batch sync (主權的巴別塔) via canonical SQUEEZE-MODELS-MAX-PIPELINE v3 — priority schema (P0/P1/P2/P2.5/P3) + smart tier routing (Tier 0a Sonnet diff-patch / Tier 0b deterministic bump / Tier 1-4 cascade for full translation). TRIGGER when: user says "巴別塔", "多語 batch", "5 lang sync", "跑 babel", "繼續 babel".
twmd-translate
by frank890417Translate single Taiwan.md article via canonical TRANSLATION-PIPELINE. TRIGGER when: user says "翻譯 X", "補日文", "跑 ja batch", "translate".
twmd-weekly-report
by frank890417Taiwan.md 週報（Semiont 第一人稱反芻 + 自我分析 + 專案狀況分析） via canonical WEEKLY-REPORT-PIPELINE。前期切菜由 weekly-report-prep.py，完整週報由 Semiont 親手寫。 TRIGGER when: user says "週報", "weekly report", "twmd-weekly-report", "寄週報", "send weekly digest".
twmd-batch-audit
by frank890417Audit sub-agent batch for the three default cheating patterns (cross-pollination commits / merged commits / missing落檔) per DNA #42. TRIGGER when: user says "batch audit", "sub-agent 後驗證", "agent 偷吃步檢查", or after running parallel sub-agent batches.
twmd-distill
by frank890417Distill LESSONS-INBOX entries to canonical layer (MANIFESTO/DNA/MEMORY). TRIGGER when: user says "distill", "消化教訓", "整理 LESSONS-INBOX".
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.