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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visual-hierarchy
by oborchersThis skill should be used when the user is establishing visual importance, designing headings, creating focal points, designing CTAs or buttons, arranging label-data relationships, implementing scanning patterns (F-pattern, Z-pattern), or ensuring one dominant element per screen. Covers the three levers of hierarchy (size, weight, color), three-tier information architecture, the 'emphasize by de-emphasizing' principle, CTA design, and label-data relationships.
consistency-design-systems
by oborchersThis skill should be used when the user is building a design system, defining design tokens, creating component libraries, implementing CSS custom properties, ensuring visual consistency across pages, choosing between design systems (Material, Ant, Carbon), or auditing a codebase for design consistency. Covers design tokens (primitive → semantic → component layers), atomic design methodology, token governance, and systematic consistency.
source-evaluation
by oborchersThis skill should be used when evaluating source credibility, deciding which search results to trust, choosing between search providers, detecting SEO spam or content farms, selecting domain-specific sources (academic, medical, legal, technical), evaluating software packages or libraries, comparing tools or technologies, assessing GitHub repo health, checking adoption metrics, or when research quality depends on retrieval quality. Covers the source credibility taxonomy (T1-T6 tiers), CRAAP framework adaptation, multi-provider search strategy, artifact evaluation framework (health/adoption/authority signals for packages, repos, APIs, standards, technologies), and source quality anti-patterns.
multi-account-from-day-one
by oborchersThis skill should be used when the user is setting up a new cloud project, designing account or project structure, creating environment isolation, configuring organization units or management groups, implementing landing zones, or deciding how to separate dev and prod workloads. Covers multi-account strategy, blast radius isolation, landing zone setup, and organizational governance.
whitespace-density
by oborchersThis skill should be used when the user is adjusting spacing, padding, margins, content density, section gaps, vertical rhythm, or separation between elements. Also applies when reviewing whether a design feels cramped or too sparse, choosing between borders and whitespace for separation, or defining a spacing system. Covers the 4px/8px spacing system, macro vs micro whitespace, content density spectrum, separation techniques (whitespace > background shifts > borders), and vertical rhythm.
packaging-distribution
by oborchersThis skill should be used when the user is building wheels, creating sdists, packaging compiled extensions, configuring cibuildwheel, setting up maturin for Rust extensions, using scikit-build-core, optimizing package size, working with platform tags, namespace packages, or choosing between pure Python and compiled distributions. Covers wheel format, abi3 stable ABI, manylinux/musllinux tags, dual-package strategy, environment markers, PyPI metadata, and TestPyPI.
structured-brainstorming
by oborchersThis skill should be used when the user needs to brainstorm, explore a problem space, think through design decisions, compare approaches, evaluate trade-offs, is stuck on an approach, or wants to explore multiple solutions. Trigger phrases include 'how should I approach this', 'what are my options', 'help me think through this', 'help me decide between X and Y', 'what are the pros and cons', 'weigh the trade-offs', 'compare these approaches', and 'I'm stuck'. Covers 8 bias-counteracting methods with user-gated parallel subagent exploration for deep dives.
caching-and-performance
by oborchersThis skill should be used when the user is implementing HTTP caching, configuring Cache-Control headers, using ETags and conditional requests, setting up CDN caching for APIs, implementing response compression, choosing between gzip and Brotli, configuring HTTP/2, or implementing circuit breakers. Covers Cache-Control directives, ETag validation, CDN strategies, compression, and resilience patterns.
hallucination-prevention
by oborchersThis skill should be used when producing any research output, verifying claims from web sources, checking citation accuracy, assessing confidence in findings, preventing hallucination cascading across agent boundaries, or reviewing research documents for factual reliability. Covers the hallucination taxonomy (7 types), OWASP ASI08 cascading failures, circuit breaker patterns, citation verification rules, confidence scoring, ground-truth validation, and known limitations of automated verification.
research-methodology
by oborchersThis skill should be used when starting any research task, decomposing a research query, planning research strategy, deciding how many sub-topics to investigate, scaling research effort to query complexity, determining when to stop researching, or dynamically re-planning based on intermediate findings. Covers query analysis, decomposition techniques (Self-Ask, Least-to-Most, DAG-based), effort scaling, plan representations, stopping criteria, and research anti-patterns.
speed-is-the-feature
by oborchersThis skill should be used when the user is building or reviewing loading states, optimistic UI updates, skeleton screens, code splitting, lazy loading, or performance budgets. Covers perceived speed, bundle size optimization, INP targets, and any work where application responsiveness and perceived latency matter.
architecture-decision-records
by oborchersThis skill should be used when the user is making significant infrastructure decisions, documenting architectural choices, creating decision records, tracking exemptions from IaC, establishing decision-making processes, or onboarding new team members to existing infrastructure. Covers ADR format, numbering, status lifecycle, exemption tracking, and decision governance.
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.