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...
workspace-digest
by cloudflareSummarize the files saved in this assistant's shared workspace. Use when the user asks what is in their workspace, for a file inventory, or a digest of saved work.
say-hi
by amilichRespond with a friendly greeting. Use when the user says hi, hello, greets the agent, or asks to be greeted.
daily
by baojie扫描并补齐缺失的每日工作日志。对比 logs/daily/ 已有日志与 git 有提交的日期（按 07:00 边界归属），列出「昨天及之前、有 commit 但无日志」的日期，逐日调用 generate_log.py 生成骨架，再按 SKILL_10b 补写微信通知和改动意义。不执行 git add/commit，不补齐今天。
tech-news-digest
by LeoYeAIGenerate tech news digests with unified source model, quality scoring, and multi-format output. Six-source data collection from RSS feeds, Twitter/X KOLs, GitHub releases, GitHub Trending, Reddit, and web search. Pipeline-based scripts with retry mechanisms and deduplication. Supports Discord, email, and markdown templates.
accessibility-migration-analysis
by adobeCreate accessibility migration analysis docs for 2nd-gen component migration. Use when on the "analyze accessibility" step for one or more components.
component-migration-analysis
by adobeCreate rendering-and-styling migration analysis docs for 2nd-gen component migration. Use when on the "analyze rendering and styling" step for one or more components.
echo
by ghostwrightBefore answering a substantive question, quietly check whether the user has already resolved this question in the past.
ecs-pr-triage
by elasticTriages an ECS pull request. Analyzes the PR diff and metadata, classifies the change (schema / tooling / docs / mixed), routes it to the correct contribution path (direct PR vs RFC Proposal vs needs-discussion), checks PR completeness, and produces a structured Triage Report.
ecs-rfc-guide
by elasticGuides contributors through the Elastic Common Schema (ECS) RFC (Proposal) process: template sections, target maturity (alpha/beta), rfcs/text artifacts, and optional OTel mapping. Use when a change needs an RFC, when drafting or reviewing RFC PRs, or when the user asks how to propose new ECS field sets or substantial schema changes.
twmd-finale
by frank890417Taiwan.md session 完整收官總指揮。盤點本 session 工作 → chain 跑 /twmd-memory + /twmd-diary + /twmd-evolve → 最後產出 final 收官 summary 給觀察者。 TRIGGER when: user says "收官", "twmd-finale", "完整收官", "session 結束 三件套", "finale".
webshop-product-detail-check
by zjunlpExamines a specific product's detailed page to verify it matches the user's requirements, checking price, description, features, and reviews. Use when you have navigated to a product detail page from search results and need to confirm the product meets all user-specified constraints before purchasing. It provides a final suitability assessment with a clear proceed-or-reject recommendation.
wordpress-content
by jezwebCreate and manage WordPress posts, pages, media, categories, tags, and menus via WP-CLI or the REST API. Use whenever the user wants to publish a blog post on WordPress, update a page, upload media, manage categories or tags, update navigation menus, schedule posts, or do bulk content operations on a WordPress site.
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.