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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grafana-generator
by diegopachecoReads the entire codebase, detects Java/Spring Boot, Rust/Actix+Axum, Python/Django, or Go/Gin, discovers all HTTP endpoints, and generates a working Grafana + Prometheus + node_exporter observability stack with podman-compose. Dashboard includes machine metrics (CPU, memory, disk, I/O, network) and application metrics (request counters, error counters, latency p50/p70/p90/p95/p99/p99.9 per endpoint).
spring-boot-helper
by diegopachecoExpert knowledge of Spring Boot 4 and Spring AI 2.0 including auto-configuration, ChatClient, advisors, tool calling, agent patterns, and Spring Framework 7 features. Use when the user asks about Spring Boot, Spring AI, dependency injection, REST APIs, agent configuration, or Spring ecosystem questions.
agent-skill-linter
by diegopachecoLint a codebase on code quality, best practices, design principles, build and test status, per-test speed (slow at or above 5s), cyclomatic complexity, and expressive naming, then render the results as a modern web report. Use when the user runs /lint or /lint-site, or asks to lint, score, or audit the quality of a project.
branch-tombs
by diegopachecoBuilds a branch graveyard for a git repository. Reads every local and remote branch, records who last touched it and when, computes age, ahead/behind, and whether it is merged into the trunk, then renders a light-theme website that shows the stale branches as headstones and lists the ones that are safe to delete. Use when the user runs /branch-tombs or asks about stale, dead, abandoned, or old branches, branch cleanup, or which branches are safe to prune.
bs-claudemd
by diegopachecoAudit a CLAUDE.md for cruft and render a light-theme report website. Reads the file line by line and flags vague rules with no checkable criterion, low-signal lines that only restate what the model already does, overlapping rules said more than once, and internal contradictions, then scores the file with a BS-o-meter and grade. Can also diff two CLAUDE.md files and flag rules that tell the model opposite things. Use when the user runs /bs-claudemd or asks to review, audit, grade, lint, or clean up a CLAUDE.md / AGENTS.md / agent instructions file, or to compare two of them.
bruno-generator
by diegopachecoScans the entire codebase, detects all HTTP/API endpoints across Java/Spring Boot, Node/Express, Go/Gin, Rust/Actix+Axum, Python/Django, and generates a complete Bruno API client project with .bru files, sample requests, and environments.
diego-pacheco-design
by diegopachecoUse this skill to generate well-branded interfaces and assets for the Diego Pacheco engineering portfolio — `https://diegopacheco.github.io/`. Suitable for production code, throwaway prototypes, additional portfolio pages, book/talk microsites, and slide decks that should match the existing look. Contains essential design guidelines, colors, type, fonts, assets, and a UI kit of React components.
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.