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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figma-make-kit-builder
by robwestzGenererar custom-kits för Figma Make (funktionen "Create a kit" / "Publish kit") på CMS-komplexitetsnivå — inga generiska SaaS-landing pages eller standardprissättningstabeller. Två lägen: (1) full-project kit med hel foundation + komponentbibliotek för en domän, (2) single-component kit som plockas in i ett befintligt projekt utan att bryta foundation. Alla kit delar samma tokens, type scale, spacing-system och layout-primitiver så komponenter kan cuttas mellan kits obrutet. Åtta färdiga seeds ingår (cms-admin-shell, workflow-builder, multi-tenant-workspace, analytics-composer, power-data-table, marketplace-console, ai-search-chat-shell, creator-lms). Stack: Tailwind + Vite (matchar Figmas starter). Use when the user wants to build something for Figma Make, upload a custom kit, publish a kit, or produce reusable component kits. Trigger on: "figma make kit", "build a figma kit", "component kit for figma", "figma make component", "publish kit", "create a kit", "figma make starter", "designsystem for figma make
systematic-research
by robwestzLocked 6-step research pipeline with hard gates that guarantees structured, cross-verified findings regardless of topic. Not ad-hoc browsing — a deterministic process: SCOPE → SOURCE → EXTRACT → CROSS-VERIFY → SYNTHESIZE → GRADE. Every step must pass before the next begins. Output is always tabular with citations, confidence scores, and explicit gap reporting. The skill exists because agents left to research freely skip sources, don't cross-verify, don't grade coverage, and produce prose instead of evidence. Use when the user needs reliable answers about technology choices, architecture patterns, competitive landscape, best practices, or any question where "I think I read somewhere" is not acceptable. Trigger on: "research", "investigate", "find out", "what's the best way to", "compare options for", "systematic research", "due diligence on", "what do experts say about", "state of the art for", "researcha", "undersök", "ta reda på". Do NOT use for: single-fact lookups (just search), code debugging (use debug s
bacowr-pipeline-v62
by robwestzBACOWR v6.2 full-stack pipeline for backlink-optimized content articles. This skill MUST be used whenever the agent produces BACOWR articles. It enforces that ALL system files (pipeline.py, models.py, engine.py, SYSTEM.md, CLAUDE.md, INIT.md) are actively used and interconnected. The agent NEVER writes solo — every article requires proven data from pipeline preflight + engine SERP intelligence + QA verification. Trigger words: artiklar, jobb, preflight, blueprint, kontextlänk, trustlink, backlink, CSV, batch, BACOWR.
bacowr-pipeline-v62
by robwestzBACOWR v6.2 full-stack pipeline for backlink-optimized content articles. This skill MUST be used whenever the agent produces BACOWR articles. It enforces that ALL system files (pipeline.py, models.py, engine.py, SYSTEM.md, CLAUDE.md, INIT.md) are actively used and interconnected. The agent NEVER writes solo — every article requires proven data from pipeline preflight + engine SERP intelligence + QA verification. Trigger words: artiklar, jobb, preflight, blueprint, kontextlänk, trustlink, backlink, CSV, batch, BACOWR.
bacowr-pipeline-v62
by robwestzBACOWR v6.2 full-stack pipeline for backlink-optimized content articles. This skill MUST be used whenever the agent produces BACOWR articles. It enforces that ALL system files (pipeline.py, models.py, engine.py, SYSTEM.md, CLAUDE.md, INIT.md) are actively used and interconnected. The agent NEVER writes solo — every article requires proven data from pipeline preflight + engine SERP intelligence + QA verification. Trigger words: artiklar, jobb, preflight, blueprint, kontextlänk, trustlink, backlink, CSV, batch, BACOWR.
200k-prompt-engineering
by robwestzThe engineering skill behind every 200k-class output. Three layers: prompt engineering (writing instructions), context engineering (designing what agents know), and agentic workflow engineering (designing how agents work autonomously). Use when writing system prompts, SKILL.md files, CLAUDE.md files, agent instructions, MCP configurations, Archon workflows, or any artifact that controls agent behavior. Trigger on: "write a prompt", "design the context", "create a workflow", "improve this skill", "optimize the instructions", "agent architecture", "context window design", "prompt engineering", "how should the agent think about this".
200k-blueprint
by robwestzTurn a product concept into a complete technical blueprint — architecture, stack, directory structure, quality gates, and the list of skills needed to build it. Use as the FIRST step before any 200k-repo build. Trigger on: "build X", "new product", "I want to create...", "200k blueprint", "design the architecture for", "what would it take to build", or any product concept that needs a structured plan before code. Also use when evaluating whether a product idea is worth building — the blueprint process reveals complexity.
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.