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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implementation
by azmankudusFull-stack implementation skill for the "Implementation (Coding)" phase of the SDLC. Provides complete project scaffolds, code generators, and convention audits for SolidJS + SolidStart (frontend) and Micronaut (backend) projects. Trigger phrases: - "scaffold project" - "create component" - "implement feature" - "generate code" - "create API endpoint" - "solidjs component" - "micronaut controller" - "add a route" - "create service" - "generate DTO" - "scaffold frontend" - "scaffold backend" - "check code conventions"
review
by azmankudusAgentic skill for the "Code Review & Collaboration" phase of the SDLC. Provides structured code review workflows, automated quality gates, convention audits, technical debt management, and PR review checklists for SolidJS + TypeScript (frontend) and Micronaut + Java 21 (backend) projects. Trigger phrases: - "review code" - "review PR" - "run lint" - "check quality" - "tech debt" - "convention audit" - "code review checklist"
blueprint-web-micronaut-solidstart
by azmankudusSpecialized blueprint for crafting full-stack web applications using Micronaut (Java 21) and SolidStart (TypeScript) for a team environment. This skill orchestrates the entire SDLC, from discovery and documentation scaffolding to build configuration (Gradle/Bun), CI/CD pipelines, and Docker containerization. Includes strict anti-hallucination guardrails and API contract governance to keep AI agents and human developers perfectly synchronized. Triggers: "blueprint web app micronaut solidstart", "new fullstack java typescript project", "setup micronaut solidstart blueprint", "generate ci cd for micronaut solidstart", "dockerize micronaut solidstart", "rancang web app java solidstart".
build-release
by azmankudusBuild & Release skill for the "Build & Release" phase of the SDLC. Provides containerization, CI/CD pipeline generation, version management, release automation, and deployment strategies for the full-stack architecture: - **Frontend**: SolidJS SSG → Bun build → nginx:stable-alpine container - **Backend**: Micronaut → Shadow JAR → Liberica CRaC container Trigger phrases: - "build container" - "create dockerfile" - "set up CI/CD" - "deploy" - "release version" - "build pipeline" - "containerize"
discovery
by azmankudusDiscovery & Requirements Gathering phase of the SDLC. Systematically elicit, analyze, and document product requirements before design or implementation begins. TRIGGER when: user mentions "gather requirements", "write PRD", "create user stories", "feasibility check", "stakeholder interview", "discovery phase", "requirements elicitation", "define scope", "product requirements", "need a spec", "what should we build", "project kickoff", "business requirements", "functional requirements", "non-functional requirements", "acceptance criteria", "domain model", "competitive analysis", "MoSCoW", "5W1H", "user story mapping", "define MVP", "scope the project", or starts a new project with ambiguous or incomplete requirements.
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.