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...
api-development
by labringFastGPT API 开发规范。重点强调使用 zod schema 定义入参和出参,在 API 文档中声明路由信息,编写对应的 OpenAPI 文档,以及在 API 路由中使用 schema.parse 进行验证。
doc-i18n
by labring将 FastGPT 文档从中文翻译为面向北美用户的英文。当用户提到翻译文档、i18n、国际化、translate docs、新增/修改了中文文档需要同步英文版时，使用此 skill。也适用于用户要求检查文档翻译缺失、批量翻译、或对比中英文文档差异的场景。
pr-review
by labring仅当用户明确手动指定使用 pr-review skill 时触发；不要因为用户传入 PR 链接、要求 review 或要求代码审查而自动触发。
pr-change-analysis
by labring手动触发的 FastGPT PR 或本地分支变更梳理技能。仅当用户显式调用 $pr-change-analysis 时使用；用于 reviewer 分析一个 GitHub PR 或当前本地分支相对 upstream/main 的需求变更、影响范围、代码质量与代码风格，不用于自动审查触发。
sealos-s3
by labringProvision, connect, and operate Sealos object storage through the sealos-cli s3 commands added in zjy365/sealos-cli#28. Use when the user needs S3-compatible storage for uploads, assets, backups, presigned URLs, bucket policy management, access credentials, quota checks, or wants to replace local MinIO/S3-compatible services with Sealos object storage in local development, Devbox, or app setup.
cloud-native-readiness
by labringAssess whether a project is ready for cloud-native deployment. Evaluates statelessness, config, scalability, and produces a readiness score (0-12). Use when user asks about containerization readiness, Docker/Kubernetes compatibility, deployment feasibility, whether their app can run in containers or the cloud, or wants a pre-deployment assessment. Also triggers on "/cloud-native-readiness".
docker-to-sealos
by labringConvert Docker Compose files or installation docs into production-grade Sealos templates. Use when user has a docker-compose.yml and wants a Sealos or Kubernetes template, wants to migrate from Docker Compose to Sealos, needs to convert container orchestration configs to Sealos format, or mentions compose-to-template conversion. Also triggers on "/docker-to-sealos".
dockerfile-skill
by labringGenerate production-ready Dockerfile for any GitHub project. Supports monorepo, multi-stage builds, workspace detection, and iterative build-fix cycles. Use when user asks to create, generate, write, fix, or improve a Dockerfile, wants to containerize an application, mentions Docker build issues, needs a .dockerignore, or wants to package their app as a Docker image. Also triggers on "/dockerfile".
sealos-app-builder
by labringBuild, adapt, and document apps that run inside Sealos Desktop using the Sealos app SDK. Use when creating a new Sealos app, integrating an existing web app into Sealos Desktop, wiring Sealos session data into business features, preparing local iframe-based debugging, or producing beginner-friendly Sealos app tutorials and starter implementations. Also triggers on "/sealos-app-builder".
sealos-canvas
by labringRun a local read-only HTML topology UI for a project already deployed by Sealos Skills and return a localhost URL. Use when the user asks to view, inspect, visualize, render, open, or run a local canvas for deployed Sealos resources, mentions ".sealos", Sealos deployment state, Kubernetes resources, topology, resource graph, localhost UI, or invokes "/sealos-canvas".
sealos-database
by labringProvision, connect, and operate Sealos Cloud databases through sealos-cli for local development, Devbox development, and app setup. Use when the user needs a cloud database for a project, asks to create or connect PostgreSQL/MySQL/MongoDB/Redis or another Sealos database, wants DATABASE_URL or similar env vars wired into a dev environment, needs database connection details, backups, logs, public access, or wants to replace local Docker Compose databases with a managed Sealos database.
sealos-deploy
by labringDeploy any GitHub project to Sealos Cloud in one command. Assesses readiness, generates Dockerfile, builds image, creates Sealos template, and deploys — fully automated. Use when user says "deploy to sealos", "deploy this project", "deploy to cloud", "deploy this repo", mentions Sealos deployment, wants to deploy a GitHub URL or local project to a cloud platform, or asks about one-click deployment. Also triggers on "/sealos-deploy".
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.