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...
i18n-lookup
by zeaburLook up Zeabur platform UI term translations from the dashboard i18n files. Use when writing or reviewing docs that reference UI elements (button labels, tab names, menu items) to ensure docs match the actual platform translations.
translate
by zeaburTranslate documentation pages to all missing locales. Use when a document exists in one locale but needs to be translated to others. Supports --dry-run, single-locale targeting, and incremental updates.
zeabur-object-storage
by zeaburUse when deploying object storage (S3-compatible) to Zeabur. Use when user needs MinIO, RustFS, or S3-compatible storage. Use when user says "object storage", "file storage", "S3", "MinIO", "RustFS", "upload files", "store files", "blob storage", or "OSS". Also use when integrating object storage with an existing service.
zeabur-template-deploy
by zeaburUse when deploying Zeabur templates or common services/databases via CLI. Use when user says "deploy template", "install template", or asks to deploy any well-known service, database, or self-hosted app. Common triggers include MongoDB, PostgreSQL, MySQL, Redis, MinIO, SurrealDB, PocketBase, WordPress, Ghost, Halo, n8n, Logto, Umami, Uptime Kuma, Vaultwarden, Prometheus, RSSHub, Miniflux, TTRSS, Memos, AFFiNE, Linkding, Slash, Bytebase, SQL Chat, ApiCat, OpenClaw, and any other open-source service the user wants to deploy to Zeabur.
zeabur-service-exec
by zeaburUse when running commands inside a Zeabur service container. Use for one-off database operations like queries, data cleanup, or migrations (e.g. mongosh, psql, mysql, redis-cli). Use when user says "exec into container", "run command in service", "query database", "delete from database", "run mongo command", "run SQL", "check files in container", "debug inside service", or "shell into service". Use for container-level debugging like checking env vars, files, processes, or connectivity. NOT for deploying databases (use zeabur-template-deploy instead).
zeabur-project-delete
by zeaburUse when deleting a Zeabur project. Use when user says "delete project", "remove project", or "clean up project". Use when tearing down test or temporary projects. Always confirm project name and ID with the user before deleting.
zeabur-restart
by zeaburUse when restarting a Zeabur service. Use when user says "restart", "reboot service", or "service is stuck/frozen".
zeabur-server-catalog
by zeaburUse when browsing available dedicated server options. Use when user asks "what servers are available", "show server prices", or "compare server plans". Do NOT use for listing owned servers (use zeabur-server-list instead).
zeabur-server-list
by zeaburUse when listing dedicated servers. Use when checking server status, IP, or provider info. Use when user says "show my servers", "SSH into server", or "check server status". Do NOT use for browsing purchasable servers (use zeabur-server-catalog instead).
zeabur-server-rent
by zeaburUse when renting a new dedicated server. Use when user wants to buy or provision a server. Supports discounted VPS from Linode, DigitalOcean, Hetzner, AWS Lightsail, GCP, Tencent Cloud (騰訊雲), Alibaba Cloud (阿里雲), and Volcano Engine (火山引擎).
zeabur-server-ssh
by zeaburUse when debugging services on a user's dedicated server via SSH. Use when needing to inspect pods, check container logs, view k8s resources, or run kubectl commands on the server. Use when "service exec" is insufficient and you need server-level access. Use when user says "check my server", "debug pod", "kubectl", "SSH into server", "check k8s", or "inspect cluster".
zeabur-service-delete
by zeaburUse when deleting a Zeabur service. Use when user says "delete service", "remove service", or "tear down service". Always confirm service name and ID with the user before deleting.
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.