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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pyramid-principle-core
by tyronerossUse when user asks about pyramid principle, Minto, SCQA, MECE, governing thought, key line, vertical/horizontal logic, or answer-first structure. Canonical rule library for structured communication.
pyramid-short-form
by tyronerossUse when user asks to draft an email, memo, executive summary, one-pager, BLUF, Slack update, or status note using the pyramid principle. Answer-first prose under ~500 words with SCQA and key line.
ui-brainstorm-preamble
by tyronerossUse at the start of /ibr:build to capture UI context before Design Director and brainstorming. Captures platform, scope, design mode, archetype, references, optional imagegen concept need, gallery target roles, density, and ask gates. Writes preamble.json.
agent-rally-point
by tyronerossUse when coordinating build-loop with peer coding agents, checking Rally Point presence/inbox state, posting handoffs or feedback, validating the embedded Rally Point boundary, or changing the future agent-rally-point spin-out surface.
agent-rally-watcher
by tyronerossUse when listening for Rally Point changes, wiring coordination watchers, debugging watch-loop behavior, or changing the future agent-rally-watcher spin-out surface.
plugin-tests
by tyronerossStatic-analysis test harness for Claude Code plugins. Triggers on "test plugin", "validate plugin", "check skill resolution", "run plugin tests", "lint plugin", "verify manifest", "namesake collision", "MCP registration check". Runs Python stdlib pytest scripts that catch namesake collisions, manifest drift, MCP misregistration, trigger-phrase coverage gaps, bridge pre-flight gaps, agent-surface drift, and cache-prune regressions. Routed as build-loop's 4th orchestrator mode (Build / Optimize / Research / Test).
agent-builder-anthropic
by tyronerossDesign, evaluate, and improve agentic harnesses — the orchestration layer around LLM-powered tools, agents, assistants, copilots, workflow runtimes, and AI-driven product features. Use this skill whenever the user mentions building an agentic system, structuring tool use, adding permissions or approval gates, designing multi-step AI workflows, managing context windows or memory, making agents durable or resumable, evaluating or pressure-testing an existing harness, planning phased implementation for an AI product, reviewing agent architecture, improving agent UX or observability, choosing between frameworks (LangGraph, CrewAI, Pydantic AI, smolagents, DSPy, AutoGen, DeepAgents), picking a memory substrate, or asking how to know if their harness is actually good. Also use when the user describes problems that imply harness gaps — agents doing unexpected things, context getting stale, sessions not surviving crashes, tools running without permission, or costs spiraling — even if they do not use the word "harness
agent-builder
by tyronerossDesign, evaluate, and improve agentic harnesses for developer tools, assistants, workflow runtimes, copilots, and AI-powered products — including agents built on local or open-source models (Ollama, llama.cpp, vLLM, Llama, Qwen, DeepSeek, Mistral). Use when work involves tool-use architecture, permissions, approval gates, workflow state, durability, context and memory systems, evaluation strategy, observability, operator visibility, framework selection (LangGraph, CrewAI, Pydantic AI, smolagents, DSPy, AutoGen, DeepAgents), memory substrate choice, or phased implementation plans for an AI system. Trigger when symptoms imply harness gaps too — stale context, surprising tool calls, sessions that die on crash, missing approval controls, costs spiraling without clear visibility, tool counts crossing ~50, context windows routinely hitting 92%+ capacity, local-model agents hallucinating tool calls, or on-device agents failing silently after model swaps.
agent-builder-codex
by tyronerossDesigns, evaluates, and improves agentic harnesses for developer tools, assistants, workflow runtimes, copilots, and AI-powered products — including agents built on local and open-source models. Applies when work involves defining or reviewing tool-use architecture, permissions, workflow state, durability, context and memory systems, evaluation strategy, observability, user experience, framework selection, or phased implementation plans for an agentic system.
reasoning-model-prompting
by tyronerossUse when prompting a reasoning model — OpenAI o-series (o3, o4-mini, o5), GPT-5 with reasoning_effort, or Anthropic Claude with extended thinking enabled. This is a counter-skill — the default prompt-builder advice (CoT, role-priming, "think step by step") actively hurts reasoning models. Encodes OpenAI and Anthropic's published 2025–2026 guidance — zero-shot first, do NOT add chain-of-thought, do NOT role-prime, developer messages replace system, reasoning_effort is a tuning knob not a quality lever, "Formatting re-enabled" recovers structured output, preserve thinking blocks across tool turns on Anthropic. Triggers on "prompt an o-series model", "prompt o3 / o4-mini / o5", "GPT-5 reasoning_effort", "extended thinking on Claude", "reasoning model", "the o-series is being weird", "the model ignores my CoT prompt", "thinking blocks", "should I use chain-of-thought here".
agent-rally-point
by tyronerossUse when working in a repository that uses Rally/Agent Rally Point for cross-agent coordination, especially at session start, before editing files, when deciding what to do next, handing work to another agent, recording facts/artifacts/decisions, resolving blockers, or coordinating with other coding agents through the `rally` CLI.
rally-workflows
by tyronerossUse when fanning out work across multiple agents, running a dynamic workflow, coordinating parallel subagents, or splitting a workstream across hosts, terminals, or machines through Agent Rally Point. Defines the workstream descriptor + task-packet protocol and the per-task rally coordination loop. Host-neutral — works for any coding agent.
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.