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...
macrofactor-mcp
by sjawharUse when reading or writing MacroFactor nutrition, food logs, weight, workout, or training program data. Triggers on meal tracking, macro counting, calorie logging, body weight, exercise logging, food search, or any MacroFactor interaction.
sbs-diet
by sjawharEvidence-based nutrition and dieting guidance for body composition and performance. Use this whenever users ask about nutrition, calories, macros, protein targets, fat loss, cutting, bulking, recomping, meal timing, intermittent fasting, keto/low-carb, processed foods, diet adherence, plateaus, metabolic adaptation, weight-change pacing, or food-choice tradeoffs. Trigger aggressively for questions about what/how much to eat, how to lose fat or gain muscle, or how to adjust diet plans over time—even when users do not explicitly ask for "diet coaching."
sbs-strength-training
by sjawharUse when someone asks how to get stronger with evidence-based programming: exercise selection, progressive overload, set/rep schemes, training volume, frequency, intensity (%1RM/RIR), hypertrophy planning, periodization, deload/fatigue management, and beginner/intermediate/advanced progression. Trigger this for workout-structure questions like "how many sets," "how often should I train," "what reps/intensity should I use," "how do I break plateaus," or "how should I set up a full program."
sbs-bench
by sjawharUse when someone asks anything about bench press form or performance: bench technique, setup, grip width, bar path, arch, leg drive, shoulder position, elbow tuck/flare, touch point, pause vs touch-and-go, incline/decline/reverse-grip choices, shoulder or elbow pain while benching, fixing sticking points, bench accessories, or programming to bench more weight.
sbs-deadlift
by sjawharUse when someone asks anything about deadlift form, deadlift technique, conventional vs sumo deadlift, setup, hip hinge, bar path, back rounding, lockout, deadlift grip, deadlift programming, fixing deadlift sticking points, Romanian deadlift, stiff-leg deadlift, deadlift accessories, or how to pull more weight.
sbs-squat
by sjawharUse when anyone asks about squatting in any capacity: squat form or technique, setup, bar position (high bar vs low bar vs front squat), stance width, toe angle, depth, knee tracking, back angle, bracing, ascent/sticking point cues, fixing squat problems (knee cave, butt wink, forward lean, balance loss), mobility limits, accessory selection, or squat programming for powerlifting, weightlifting, bodybuilding, or general strength.
skiplagged
by sjawharUse when searching flights, hotels, or rental cars; comparing fares across flexible dates; discovering cheap destinations from a fixed origin; or hunting hidden-city ticketing deals. Trigger on multi-city itineraries, fare calendars, "where can I fly cheaply", price-sensitive trip planning, or any time the user wants a sanity-check against Google Flights pricing — Skiplagged surfaces hidden-city deals other engines deliberately hide.
product-demos
by sjawharUse when creating narrated product demo videos from terminal recordings. Triggers on: asciinema, screen recording, product video, demo video, narrated walkthrough, voiceover, TTS, cast-to-video, product announcement with video
legion-controller
by sjawharUse when coordinating Legion workers across issues, dispatching workers, monitoring progress, or routing triage items
legion-oracle
by sjawharResearch institutional knowledge before escalating questions to users. Check docs/solutions/ and codebase patterns before asking humans.
legion-worker
by sjawharUse when dispatched by Legion controller to work on an issue in a jj workspace
legion-retro
by sjawharCapture learnings from completed work via dual-perspective retrospective. Invoked by resuming an implement worker session — the implementer has full context, and a fresh subagent provides an outside view.
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.