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...
background-bash
by weikinhuangWHAT: Decide when to run a shell command off-turn with the `bg_bash` tool instead of blocking the turn on the built-in `bash` tool, and how to poll / steer / collect it across turns. WHEN: A command is long-running (dev server, build, watch, test suite, training run, log tail) or you want the turn free while it runs. DO-NOT: Background a quick command whose output you need immediately, spawn a job you never poll, or use it for pure computation - that is `compute`.
illustrious-prompting
by weikinhuangPrompting rules for the OnomaAI **Illustrious-XL** SDXL anime / illustration model. Use when the user asks to generate / draw / render an anime / illustration image on an Illustrious-XL workflow (v0.1, v1.0, or a near-vanilla finetune). Illustrious takes plain Danbooru tags + natural language - no Pony score tags, no Anima `@artist` prefix. Do not use for photorealism.
bin-script-scaffold
by weikinhuangWHAT: Scaffold a new bin script in this dotfiles repo with the required sibling files: the script itself, a bash completion file, and a bats test. WHEN: User asks to add a new git subcommand, utility script, or any other executable under dotenv/bin or a platform-specific bin directory. DO-NOT: Skip the completion file or the bats test stub; invent a new argument-parsing style for flag-taking scripts; or add scripts to external/ or to private `__`-prefixed paths without confirming intent.
chenkin-noob-xl-prompting
by weikinhuangPrompting rules for **Chenkin Noob XL (CKXL)**, a NoobAI-XL-1.1 *eps* fine-tune. Use when the user asks to generate / draw / render an anime image on a CKXL workflow (v0.2, v0.5, or near-vanilla finetune). CKXL is eps (not v-pred) and inherits NoobAI's `artist:name` syntax and anti-furry negative conventions; do not conflate with the NoobAI v-pred skill.
iterate-until-verified
by weikinhuangUse the `check` tool to run a disciplined feedback loop whenever the task is "produce an artifact and confirm it satisfies a verifiable contract" - render an SVG / chart / diagram, generate a config / regex / test / fixture / snippet that has to match a spec, write code that has to pass a test suite, produce prose a critic can rubric-judge, or "make a Y that does Z". Never claim the artifact is done without a passing verdict from `check run` this turn.
oxlint-typescript-conventions
by weikinhuangWHAT: Write TypeScript under `lib/node/`, `tests/`, and `config/pi/` that passes the repo's strict `oxlint --type-aware` configuration on the first try, so the husky pre-commit hook does not reject the commit. WHEN: User asks to add or edit a `.ts` / `.spec.ts` file anywhere outside `config/pi/extensions/` (the extensions tree has a relaxed override). DO-NOT: Bypass the rules with `// oxlint-disable` instead of fixing the code.
noobai-vpred-prompting
by weikinhuangPrompting rules for the Laxhar Labs **NoobAI-XL v-prediction** anime / illustration model. Use when the user asks to generate / draw / render an anime image on a NoobAI v-pred workflow. NoobAI uses `artist:name` syntax and is e621-trained (requires anti-furry negatives); the v-prediction variant has stricter sampler and CFG constraints than the eps variant - do not conflate them.
hooks-author
by weikinhuangWhen to reach for a user hook (`~/.pi/agent/hooks.json` / `<repo>/.pi/hooks.json`) over an ad-hoc bash command or a full pi extension. Use when the user asks things like "can I run X every time pi does Y", "from now on when Z happens, do W", "log every bash command", "format files after edit", "inject context into every prompt", or asks to wire a Claude Code hook into pi. Do not suggest a hook for one-shot tasks, to replace a built-in gate, or for behavior that needs new pi events or tool registration.
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.