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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engineer-analyst
by rysweetAnalyzes technical systems and problems through engineering lens using first principles, systems thinking, design methodologies, and optimization frameworks. Provides insights on feasibility, performance, reliability, scalability, and trade-offs. Use when: System design, technical feasibility, optimization, failure analysis, performance issues. Evaluates: Requirements, constraints, trade-offs, efficiency, robustness, maintainability.
consensus-voting
by rysweetMulti-agent consensus voting with domain-weighted expertise for critical decisions requiring structured validation
urban-planner-analyst
by rysweetAnalyzes urban development through planning lens using zoning, land use, comprehensive planning, and transit-oriented development frameworks. Provides insights on spatial organization, infrastructure, sustainability, and livability. Use when: Urban development projects, zoning decisions, transportation planning, sustainability initiatives. Evaluates: Land use patterns, density, accessibility, environmental impact, community needs.
environmentalist-analyst
by rysweetAnalyzes events through environmental lens using ecological principles, systems thinking, sustainability frameworks, and conservation biology to assess ecosystem health, biodiversity impacts, and long-term environmental sustainability. Provides insights on climate change, resource management, pollution, habitat conservation, and human-nature relationships. Use when: Environmental policy, climate decisions, conservation planning, resource extraction, pollution assessment. Evaluates: Ecosystem health, biodiversity, sustainability, climate impacts, carrying capacity, environmental justice.
code-atlas
by rysweetBuilds comprehensive, living code-atlases as multi-layer architecture documents derived from code-first truth. Defaults to both Graphviz DOT and Mermaid. User can override to single format. Language-agnostic (Go, TypeScript, Python, .NET, Rust, Java). Files issues with 'code-atlas-bughunt' label. Treats atlas-building as a multi-agent bug-hunting journey: graph-form reasoning exposes structural bugs, route/DTO mismatches, orphaned env vars, dead code paths, and stale documentation that linear review misses. Three-pass bug hunt with per-journey PASS/FAIL/NEEDS_ATTENTION verdicts. Use when: creating architecture documentation, investigating unfamiliar codebases, hunting structural bugs, setting up CI/CD diagram refresh, or publishing to GitHub Pages/mkdocs.
historian-analyst
by rysweetAnalyzes events through historical lens using source analysis, comparative history, periodization, causation, continuity/change, and contextualization frameworks. Provides insights on historical patterns, precedents, path dependency, and long-term trends. Use when: Understanding historical context, identifying precedents, analyzing change over time, comparative history. Evaluates: Causation, continuity, change, context, historical parallels, long-term patterns.
anthropologist-analyst
by rysweetAnalyzes events through anthropological lens using cultural analysis, ethnographic methods, kinship and social organization, symbolic systems, ritual and practice, and comparative ethnology. Provides insights on cultural meanings, social practices, symbolic structures, cultural change, and cross-cultural patterns. Use when: Cultural conflicts, identity issues, ritual significance, symbolic meanings, cultural change, cross-cultural comparison. Evaluates: Cultural systems, symbolic meanings, social practices, kinship structures, cultural adaptation, power-culture nexus.
indigenous-leader-analyst
by rysweetAnalyzes events through indigenous knowledge systems using relational thinking, seven generations principle, reciprocity, holistic integration, and traditional ecological knowledge frameworks. Provides insights on interconnectedness, long-term sustainability, collective wisdom, and decolonial perspectives. Use when: Environmental decisions, resource stewardship, community governance, decolonization, intergenerational planning. Evaluates: Relationships, sustainability, collective impact, indigenous rights, traditional knowledge integration.
political-scientist-analyst
by rysweetAnalyzes events through political science lens using IR theory (Realism, Liberalism, Constructivism), comparative politics, institutional analysis, and power dynamics. Provides insights on governance, security, regime change, international cooperation, and policy outcomes. Use when: Political events, international crises, elections, regime transitions, policy changes, conflicts. Evaluates: Power distributions, institutional effects, actor interests, strategic interactions, norms.
oxidizer-workflow
by rysweetAutomated Python-to-Rust migration via iterative convergence loops. Treats the Python codebase as the living specification and produces a fully-tested Rust equivalent with zero-tolerance parity validation. Use when migrating Python modules, libraries, or CLIs to Rust. Activates for: migration, oxidize, python to rust, port to rust, rewrite in rust.
dotnet-install
by rysweet.NET SDK and runtime installation across Windows, macOS, and Linux. Handles version detection, platform-specific installers (WinGet, Homebrew, apt, dnf), SDK vs runtime selection, offline installation, Docker setup, and troubleshooting. Auto-activates for .NET installation, setup, version management, and multi-platform deployment.
computer-scientist-analyst
by rysweetAnalyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.
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.