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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knowledge-searching
by mzt-76Nutrition science Q&A — facts, definitions, recommendations. MANDATORY for any factual nutrition question. Triggers: "c'est quoi / what is", "is it good to / est-ce bon de", "combien de protéines / how much protein", "macros", "déficit / deficit", "suppléments / supplements", "régimes / diet", "BMR / TDEE", "explique / explain a nutrition concept". NOT for recipe generation or meal planning (use meal-planning) or food logging (use food-tracking). Always call retrieve_relevant_documents BEFORE answering.
body-analyzing
by mzt-76Body composition analysis from a photo (body fat % estimation). Triggers: "analyse ma photo / analyze my photo", "estime mon body fat / estimate my body fat", "taux de masse grasse / body fat percentage", "composition corporelle / body composition", or any image attachment of the user's body.
nutrition-calculating
by mzt-76Calculate BMR, TDEE, macro and calorie targets from biometrics + goals. Triggers: "calcule mes besoins / calculate my needs", "mon BMR / TDEE", "combien de calories / how many calories should I eat", "mes macros / my macros", "objectif perte de poids / weight loss goal", "prise de masse / muscle gain", "recalcule / recalculate my targets". Auto-infers goals from context.
weekly-coaching
by mzt-76Weekly check-in with trend analysis, pattern detection, and personalized nutrition adjustments. Triggers: "bilan de la semaine / weekly review", "weekly check-in / coaching", "mon poids / my weight this week", "ajustements / adjustments", "comment ça avance / how am I doing", "fatigue / faim", "set baseline / définir baseline".
meal-planning
by mzt-76Meal plans, recipes, and recipe favorites. Load this skill when the user asks for: plan repas / meal plan, recette / recipe, idée de plat / dish idea / suggestion, menu de la semaine / weekly menu, "génère un plan" / "create a plan", recette spécifique (e.g. "crêpes au saumon", "poulet curry"), gérer favoris / manage favorites — ajouter / save / "add to favorites", lister / list / "mes favoris" / "my favorites", chercher / find / "trouve la recette", supprimer / delete / remove a favorite. 2 routes: DB recipes (fast default) or custom LLM (when a specific dish is named).
project-audit
by mzt-76Audit complet du codebase avant deploiement. Analyse tout le code sur 6 dimensions (simplicite, modularite, efficacite, securite, correctitude, conformite aux standards) via une equipe de 4 agents coordonnes (Agent Teams) qui se cross-review mutuellement. Produit un rapport detaille et un plan de fixes compatible /execute. Triggers: "audit", "code review complet", "pre-deployment review", "check code quality", "find all issues", "audit complet", "revue de code", "analyse du projet"
seed-recipes
by mzt-76Seed the recipe database with new healthy/sporty recipes that fill identified gaps. Use when the user wants to add recipes, enrich the recipe DB, fill coverage gaps, or improve variety. Triggers on requests like "ajoute des recettes", "seed recipes", "enrichir la base de recettes", "combler les gaps". Launches a background worktree subagent that analyzes gaps, creates recipes from OFF-validated ingredients only, and inserts them.
skill-creator
by mzt-76Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
food-tracking
by mzt-76Log food eaten, view daily intake, manage food journal entries. Triggers: "j'ai mangé / pris", "I ate / had", "log breakfast / lunch / dinner / snack", "log my [meal]", "petit-déj / déjeuner / dîner / collation", "ajoute à mon tracker", "add to my tracker", "enregistre / log this meal", "mets dans mon suivi", "onglet Suivi du Jour", "mon bilan / daily summary", "combien il me reste ?", "what did I eat", "qu'est-ce que j'ai mangé", "modifier / edit an entry", "supprimer / delete / remove an entry from my log". 3 scripts: log_food_entries (write), get_daily_summary (read), delete_food_entry (delete).
shopping-list
by mzt-76Generate categorized shopping list from a stored meal plan or recipes. Triggers: "liste de courses / shopping list", "qu'est-ce que je dois acheter / what do I need to buy", "ingrédients à acheter / ingredients to buy", "courses / groceries", "fais ma liste / make my list".
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.