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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dtn-leo-connectivity
by AmnadTaowsoamThis skill covers the implementation and management of network protocols for space-based communications, specifically focusing on Low Earth Orbit (LEO) satellite constellations and Disruption-Tolerant
discount-promotions
by AmnadTaowsoamDiscount and promotion engine manages coupon codes, promotional rules, discount calculations, validation, and analytics for e-commerce platforms. Effective promotion systems support multiple discount
mui-material
by AmnadTaowsoamMaterial-UI (MUI) is a comprehensive React component library implementing Google's Material Design system. It provides 50+ production-ready components with built-in accessibility, responsive design, a
hybrid-inference-architecture
by AmnadTaowsoamHybrid Inference Architecture enables intelligent coordination between cloud and edge inference systems, dynamically routing inference requests based on latency requirements, model complexity, resourc
infinite-scroll
by AmnadTaowsoamInfinite scroll is a technique for displaying large datasets by loading additional content as the user scrolls to a predefined threshold, instead of loading all data at once. This skill covers Interse
mlflow-patterns
by AmnadTaowsoamMLflow is an open-source platform for managing complete ML lifecycle, including experiment tracking, model packaging, model registry, and deployment. It enables data science teams to collaborate and d
timescaledb
by AmnadTaowsoamTimescaleDB is a time-series database optimized for fast ingest and real-time analytics. It provides automatic time-based partitioning, built-in compression, and continuous aggregates, making it ideal
incident-severity-levels
by AmnadTaowsoamIncident Severity Levels provide a standardized framework for classifying incidents based on their impact on users, business operations, and SLA compliance. Consistent severity classification ensures
severity-levels
by AmnadTaowsoamSeverity levels provide a standardized way to classify incidents based on their impact, enabling appropriate response, resource allocation, and communication. Consistent severity classification ensure
api-design-contracts
by AmnadTaowsoamAPI contract-first design using OpenAPI/Swagger for REST and AsyncAPI for events to create clear contracts, support backward compatibility, and enable contract testing between services. This skill ena
strapi-integration
by AmnadTaowsoamStrapi is an open-source headless CMS built with Node.js. This guide covers setup, content types, customization, and integration patterns for building content-driven applications with a developer-frie
mqtt-integration
by AmnadTaowsoamMQTT (Message Queuing Telemetry Transport) is a lightweight publish/subscribe messaging protocol designed for IoT and low-bandwidth, high-latency networks. It provides a simple and efficient way to co
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.