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.
Querying local SQLite index...
caddy-https-troubleshoot
by dawiddutoitDiagnoses and fixes HTTPS/SSL certificate issues in the network infrastructure by checking API tokens, validating Caddy configuration, and testing certificates. Use when certificates are not obtained, HTTPS is not working, seeing SSL errors, "Invalid Authorization header", or Caddy restart loops. Triggers on "HTTPS not working", "SSL certificate issue", "certificate error", "Caddy not starting", "fix HTTPS", "troubleshoot certificates", or "Invalid format for Authorization header". Works with Caddy, Cloudflare DNS-01 challenge, and Let's Encrypt.
gradle-spring-boot-integration
by dawiddutoitConfigures Gradle with Spring Boot projects including plugin setup, bootable JAR creation, layered JARs for Docker optimization, and multi-module Spring Boot configurations. Use when asked to "set up Spring Boot with Gradle", "create executable JARs", "configure Docker layering", or "set up Spring Boot microservices".
internal-comms
by dawiddutoitWrites internal communications using company-specific formats including 3P updates (Progress, Plans, Problems), newsletters, FAQs, status reports, leadership updates, incident reports, and project updates. Use when asked to "write a status report", "create a 3P update", "draft a company newsletter", "write an incident report", "create leadership update", or "draft project update". Provides templated guidelines for tone, structure, and content gathering specific to each communication type. Works with markdown documents following established company communication standards and formatting preferences.
ha-graphs-visualization
by dawiddutoitCreates and configures Home Assistant graph visualizations using history-graph, statistics-graph, mini-graph-card, and apexcharts-card with time ranges, aggregations, and multi-sensor support. Use when displaying sensor data over time, creating trend charts, comparing historical data, or building energy/climate/air quality dashboards.
textual-reactive-programming
by dawiddutoitBuilds reactive Textual widgets using computed properties, watchers, and reactive attributes. Use when implementing data-binding patterns, responding to state changes, computed properties, efficient updates based on attribute changes, and validation patterns. Covers reactive decorators, watch methods, and avoiding manual refresh patterns.
brand-guidelines
by dawiddutoitApplies Anthropic's official brand identity including colors (Dark #141413, Light #faf9f5, Orange #d97757), typography (Poppins, Lora), and design standards to artifacts, presentations, documents, and web interfaces. Use when asked to "apply Anthropic branding", "use brand colors", "style with company guidelines", "add Anthropic look-and-feel", or "follow design standards". Provides official color palettes, font specifications, logo usage, and visual formatting rules. Works with HTML artifacts, presentations, PDFs, documents, and any visual deliverables requiring consistent brand identity.
manage-agents
by dawiddutoitCreates, modifies, and manages Claude Code subagents by writing agent files with YAML frontmatter, system prompts, and tool configurations. Use when you need to "create an agent", "modify an agent", "set up a specialist", "I need an agent for [task]", "agent to handle [domain]", or "configure agent tools". Covers agent file format, YAML frontmatter, system prompts, tool restrictions, MCP integration, model selection, and testing.
python-test-micrometer-testing-metrics
by dawiddutoitTests custom Micrometer metrics in unit and integration tests using SimpleMeterRegistry. Use when writing tests for services that record metrics, validating metric values after operations, testing percentiles and histograms, or asserting metric behavior without full Spring context. Essential for ensuring metrics are accurately recorded in business logic.
openscad-collision-detection
by dawiddutoitDetects and visualizes geometric intersections in OpenSCAD models using built-in techniques (intersection(), debug modifiers), BOSL2 utilities (debug_this, ghost), and reusable collision check patterns. Use when checking drawer clearances, door swing interference, shelf spacing, hinge collisions, or any geometric overlap in woodworking projects. Triggers on "check collision", "detect interference", "drawer clearance", "door swing", "check overlap", "assembly interference", or "fits in cabinet". Works with .scad files, woodworkers-lib patterns, BOSL2 attachments, and OpenSCAD 2025 Manifold engine.
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.