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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zhipu-coding-plan
by Lingtai-AINested swiss-knife reference for Zhipu / Z.AI coding-plan capabilities. Use the Zhipu / Z.AI GLM coding-plan subscription as a unified backend for vision (multimodal screenshot/diagram analysis), web search, web page reading, and zread (open-source GitHub repo browsing). One API key unlocks all four MCP servers. This skill is a thin pointer: it tells you how to source the key, pick the region (Z.AI international vs BigModel mainland), and where the live docs are. MCP server registration is owned by the `mcp-manual` skill (kernel capability).
xiaomi-mimo
by Lingtai-AINested swiss-knife reference for Xiaomi MiMo provider discovery. Discovery protocol (not a reference) for Xiaomi MiMo (小米MiMo) — an OpenAI-/Anthropic-compatible LLM provider whose single API key unlocks a family of ~9 models behind one chat-completions endpoint. The family spans long-context text reasoning, multimodal-input chat (image / audio / video understanding via standard `messages.content[]`), and a text-to-speech line that returns base64 audio (with built-in voice catalogue, voice-design-from-prompt, and voice-cloning-from-sample variants). Marketing surface lives at https://mimo.xiaomi.com; the developer docs are at https://platform.xiaomimimo.com — and the agent should fetch the live docs rather than trust this manual for anything schema-shaped, because the API surface evolves. This skill teaches the agent (1) which two URLs to start from, (2) how to enumerate the current model lineup, (3) how to source the API key from the user's preset library, (4) how to pick the right base URL for their key (pay
dev-guide-gotchas
by Lingtai-AINested lingtai-dev-guide reference for known implementation footguns: Bubble Tea v2 paste, textarea theming, dev-mode rebuilds, editable installs, migrations, localization, authorization gates, and config conventions.
web-browsing
by Lingtai-AIFetch, extract, scrape, or search web content. First try `python3 <skill-path>/scripts/extract_page.py <URL>`: it auto-tiers across PDFs, metadata APIs, trafilatura, BeautifulSoup, Playwright, Jina, and AI search. Read this router when the script fails, you need site/tier routing, or you are composing a multi-step web/research pipeline.
web-browsing-maintenance-bundles
by Lingtai-AINested web-browsing reference for maintenance protocol, semantic sweeps, dirty-first testing, bundled JSON asset files, deep-dive reference files, and the explicit decision flowchart.
web-browsing-routing-and-sites
by Lingtai-AINested web-browsing reference for auto-tier decisions, per-site tier recommendations, known limitations/gotchas, and real-time data endpoints.
web-browsing-tier-quick-refs
by Lingtai-AINested web-browsing reference for tier quick-reference commands: Tier 0 PDF, Tier 1 APIs, Tier 1.5 Trafilatura, Tier 2 BeautifulSoup, Tier 3 Playwright stealth, Tier 4 Jina/Firecrawl, and Tier 5 AI-native search.
dev-guide-architecture
by Lingtai-AINested lingtai-dev-guide reference for the project architecture: Go TUI/portal monorepo, Python kernel, MCP addon repos, filesystem IPC, and where per-project/per-machine state lives.
dev-guide-debug-troubleshoot
by Lingtai-AINested lingtai-dev-guide reference for diagnosing LingTai failures: agent process state, OOM/crashes, avatar spawn issues, post-molt memory loss, mail delivery, scheduled messages, tool timeouts, and escalation.
dev-guide-network-governance
by Lingtai-AINested lingtai-dev-guide reference for avatar network governance: core rules, health monitoring, CPR, watchdog cadence, role documentation, delegation patterns, workspaces, and peer communication.
dev-guide-releasing
by Lingtai-AICompact lingtai-dev-guide release overview: when you are doing a release, the maintainer-authorization boundary, the version scheme, and a pointer to release-workflow for the full TUI/Portal + kernel publishing checklist, GitHub/PyPI/Homebrew steps, the required HTML release log, and the website release blog.
dev-guide-security-audit
by Lingtai-AINested lingtai-dev-guide reference for security audits: secret scanning, file permissions, MCP config, communication security, data exposure, agent permission review, severity classification, and report format.
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.