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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api-tool-add
by pardnchiuConvert user-supplied API source (Swagger/OpenAPI JSON, cURL, or natural-language endpoint description) into Agenvoy APIDocumentData format and write each endpoint as a separate JSON file under `~/.config/agenvoy/tools/api/`. Triggers on requests like "add an API tool", "新增 api tool", "把這個 swagger 轉成 api tool", "註冊一個 API 給 agent 用". Handles auth schema (bearer / apikey / basic) and warns on intranet/localhost hosts before write.
code-reviewer
by pardnchiuAnalyze project source code and generate optimization suggestions. Use when user wants code review, performance optimization advice, security hardening recommendations, or architecture improvement suggestions.
extension-install
by pardnchiuInstall an Agenvoy extension from pkg.agenvoy.com registry (browse/pick) or local tarball into ~/.config/agenvoy/tools/.extension/<type>/<name>@<version>/. Extracts tar.gz, validates manifest (email field, type api/script only), installs deps, stores keychain keys, atomically moves staged dir. Collisions handled by Overwrite/Rename/Cancel popup.
extension-upload
by pardnchiuPackage a script tool under ~/.config/agenvoy/tools/script/ into a tar.gz and publish to pkg.agenvoy.com registry. Keyword picker, dep/key detection, config-stored email (ask + lowercase + persist), ask version, email verification gate, multipart upload with downgrade/unique guards.
improve-skill
by pardnchiuImprove a skill's SKILL.md and scripts/*.md based on execution trace errors. Fixes tool-name references, unclear steps that caused failures, and wording issues. Writes corrected files to ~/.config/agenvoy/skills/<name>/.
plan
by pardnchiu將模糊需求煉成可執行計畫並立即執行。僅由 `/plan {需求}` 顯式觸發。流程：呼叫 `ask_user` tool 依需求複雜度蒐集多面向資訊（核心 3 維度為底，依風險／不可逆性／資源缺口擴充至 5-7 題）→ 呼叫 `generate_plan` tool 產出結構化計畫 → 視計畫缺口 ask_user 補問 或 直接逐步執行。第一個 tool call 永遠是 `ask_user`；不自己組計畫，必走 `generate_plan` tool。
readme-generate
by pardnchiu從原始碼分析自動生成雙語 README。當使用者請求為專案建立 README、需要從程式碼庫生成 README.md（英文）和 README.zh.md（中文）、或希望為其函式庫/套件建立一致的多語言文件時使用。
scheduler-skill-creator
by pardnchiu建立並排程定時觸發的 skill。**所有新增定時／週期任務、提醒、排程通知的請求必須走此 skill**，禁止直接呼叫 add_schedule（那是 skill 已存在時的時間綁定工具，不該作為新建排程的入口）。 必定觸發的訊息特徵（任一即活化）： - 相對延遲：「X 分鐘後」「X 小時後」「稍後」「待會」「等一下」 - 明確時間：「X 點」「下午 X 點」「明天 X 點」「後天」「YYYY-MM-DD HH:MM」 - 週期性：「每 X 分鐘」「每小時」「每天」「每週」「每月」「定時」「固定」 - 提醒 / 通知意圖：「提醒我」「通知我」「告訴我」+ 時間描述 範例觸發訊息：「5 分鐘後提醒我喝水」「每天早上 9 點抓 HN 頭條」「明天下午 3 點開會」「每 5 分鐘查台積電股價」。 **不觸發**（即使訊息含「觸發」「排程」字眼也不 activate）： - 訊息含 `[執行已存在 scheduler skill:` 標記 → 為 `/sched-<name>` 手動 trigger，當前 agent 直接執行 body - 訊息為一份完整的 SKILL.md body（`# Title` + `## 任務` + `## 輸出格式` 結構），無建立／排程動詞 → 為 skill execution，非 creation - 訊息僅含「執行 skill X」「跑 X」「run skill X」無時間 token → 為 execution 流程：解析訊息抽出「要做什麼」「何時觸發」→ 缺項用 ask_user 補問 → 生成 skill 檔案至 ~/.config/agenvoy/skills/scheduler/<short>-<hash8>/SKILL.md（無 scheduler- 前綴，hash 用於避免命名衝突）→ 呼叫 add_schedule 綁定時間 → 回報。
script-tool-add
by pardnchiuDesign and scaffold a Python (or JavaScript) script tool for Agenvoy, writing `tool.json` + `script.py`/`script.js` pair under `~/.config/agenvoy/tools/script/<tool_name>/`. Triggers on requests like "add a script tool", "幫我寫一個 python tool", "做一個 script tool 給 agent 用", "新增腳本工具". Handles parameter schema design, stdlib-vs-third-party dependency check, keychain secret access via local `/v1/key` endpoint, sandbox awareness, and test execution before write.
search-suitable-public-api
by pardnchiuSearch the curated Agenvoy public API list for an API that fits the current user need or skill context, then chain into the `api-tool-add` skill to register it under `~/.config/agenvoy/tools/api/`. Triggers when the agent lacks a tool for a data lookup (weather, currency, geocoding, dictionary, etc.), when the user says "找個 API"／"有沒有 XXX 的 API"／"我需要查 XXX 但你沒工具"／"add a public API for this", or when an upstream skill needs an external data source that is not yet wired.
skill-creator
by pardnchiuCreate, edit, improve, or audit AgentSkills. Use when creating a new skill from scratch or when asked to improve, review, audit, tidy up, or clean up an existing skill or SKILL.md file. Also use when editing or restructuring a skill directory.
tool-reviewer
by pardnchiuAudit Agenvoy tool definitions (built-in Go tools, extensions/apis/*.json, extensions/scripts/*/tool.json) against the project's tool design rules under the lazy-schema model — name clarity, description trigger coverage, schema field completeness, English-only text, and explicit defaults on optional fields. Use when the user wants to review tool quality, check tool design compliance, or audit api_/script_ extensions.
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.