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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viral-infographic-az
by jreagan007Visual asset production for AZ Law Now Tier-S pillars using the Fractl composite-long-scroll methodology, themed for the Sunset Editorial palette (Newsprint cream, Golden Hour accent, Alert Vermillion urgent, Burnt Sienna warm secondary, Headline Black, Dusk Slate body). Takes a fact-bundle slug + byline + psychographic primary audience, produces a canonical design brief at docs/design/<slug>-infographic.md for the human-in-the-loop Figma build, then orchestrates the critique to v1 to cut-asset to embed pipeline. Bakes in AZ Law Now brand-messaging integration (You Get Answers tagline, AZ Law Now lockup placement, Cormorant Garamond headings, DM Sans body), viral guardrails (no manufactured fear, no legal CTA on visual, person-first across alt text), and psychographic discipline per audience. Use this skill whenever building the visual asset for an AZ Tier-S piece classified per docs/strategy/AUTONOMOUS-OPERATING-STRATEGY-AZ.md. Triggers for "build the infographic for X," "Fractl visual for X," "composite lon
pillar-architect-az
by jreagan007Plan and ship a content cluster (pillar + spokes) for AZ Law Now using a gated Discovery to Positioning to Engineering to Verification protocol, built on the "don't be found, be needed" selection filter and an Arizona primary-source data backbone competitors cannot replicate. Use before starting any multi-article cluster expansion (e.g., "build out the elder-care cluster," "expand school-restraint spokes," "deepen the corridor-crash cluster," "add 8 new ARS statute explainers"). Triggers when a request involves planning >2 articles in a related cluster, when ranking recovery requires topical-authority depth, when ADOT corridor data supports a new practice-area pillar, when city-page programmatic value is in question, or when a four-way-anchor cross-link gap is identified. The four-way anchor (Brendan investigation + Brandon legal guide + Stephanie client guide + practice-area page) is the AZ Law Now cluster atom, and it is not optional.
pi-investigation
by jreagan007End-to-end Brendan Franks data-journalism protocol for src/content/investigations/. Covers ADOT corridor studies, FRA grade-crossing histories, AHCCCS/CMS nursing-home violation audits, DCS daycare records, ADE school-discipline data, FMCSA ghost-fleet chains, municipal hot-spot analyses, and legal-ad-spend concentration studies. Use this skill before writing any investigation-bylined piece. Pre-publish gate, legal-fact-check must run and exit 0 before commit. Triggers any Brendan-voice investigation, any public-records-driven piece, any data-first ARS 39-121 request, any Ideation Gate or Finding Gate evaluation, any concentration or citation-divergence study. SKIP for single-source news briefs with no original data finding, and for Brandon or Stephanie content (investigations are Brendan-only).
pi-cluster-architect
by jreagan007Plan and ship a content cluster (pillar + spokes) for AZ Law Now using a structured Discovery → Positioning → Engineering → Verification protocol. Use this skill before starting any multi-article cluster expansion involving ≥2 related articles across the five content collections (investigations, legal-guides, client-guides, practice-areas, glossary). Trigger when: a request involves planning a cluster, when ranking recovery requires topical authority depth, when ADOT corridor data supports a new practice-area pillar, when city-page programmatic value is in question, or when a four-way anchor cross-link gap is identified. The four-way anchor (Brendan investigation + Brandon legal guide + Stephanie client guide + practice-area page) is the AZ Law Now cluster atom, and it is not optional. The selection filter is "don't be found, be needed": a cluster ships only if it produces irreducible value a model would have to cite, built on an Arizona primary-source data backbone competitors cannot replicate.
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.