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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generate-shopping-list
by richardblythman**Shopping List Generator**: Consolidates ingredients from a weekly meal plan into an organized, deduplicated shopping list grouped by grocery category. Use this skill whenever the user wants to create a shopping list from their meal plan, needs to know what to buy for the week, wants to update an existing shopping list after meal plan changes, or asks about ingredient quantities for planned meals. Also trigger when the user says "what do I need to buy", "shopping list", "grocery list", "ingredients for this week", or references buying food for planned meals.
design-elimination-diet
by richardblythman**Elimination Diet Planner**: Designs structured elimination diets to test suspected food triggers, with specific meal plans that remove target ingredients while maintaining nutritional balance and variety. Use this skill when the user wants to test whether a food is triggering symptoms, design a FODMAP elimination phase, plan a reintroduction protocol, create an elimination diet, or structure a food sensitivity test. Also trigger when the user says "I want to cut out X and see what happens", "elimination diet", "FODMAP elimination", "reintroduction plan", "test if X is a trigger", or discusses removing specific foods to test their impact on symptoms.
analyze-symptoms
by richardblythman**Digestive Symptom Analyzer**: Correlates Bristol scale and symptom log entries with meals consumed in the preceding 24-48 hours to identify potential food triggers and patterns. Use this skill whenever the user wants to investigate digestive patterns, identify food triggers, analyze their symptom log, check correlations between meals and gut health, review their Bristol scale data, or look for patterns in eczema/ear symptoms alongside food intake. Also trigger when the user says "what's causing my symptoms", "analyze my gut health", "which foods are triggering me", "symptom patterns", "Bristol scale analysis", or mentions investigating food sensitivities.
plan-weekly-meals
by richardblythman**Meal Plan Builder & Nutrition Analyzer**: Takes your meal selections for the week and produces a complete meal plan with day-by-day breakdown, full nutritional analysis (macros, micros, daily averages), and preparation notes. Use this skill whenever the user wants to plan meals for a week, analyze the nutrition of selected meals, check if their diet has gaps, get suggestions for meal improvements, or create a weekly meal plan. Also trigger when the user mentions "what should I eat this week", "plan my meals", "nutrition analysis", "is my diet balanced", "meal plan", "weekly meals", or describes a set of meals they want to eat over multiple days.
add-recipe
by richardblythman**Recipe Formatter & Adder**: Takes a recipe from any source (URL, photo, description, cookbook reference) and creates a properly formatted, nutrition-calculated recipe file in the knowledge base. Also creates any missing component files for ingredients. Use this skill whenever the user wants to add a new recipe, save a recipe they found, format a recipe for the database, log a meal they cooked, create a recipe from ingredients, or build out their recipe collection. Also trigger when the user shares a recipe link, photo of a recipe, or describes a meal they want to add. Trigger for "add recipe", "save this recipe", "I made X tonight and want to save it", "format this recipe", "new recipe", or when the user shares food-related URLs or images.
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.