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...
elodin-dev
by elodin-sysDevelop and contribute to the Elodin codebase. Use when building Elodin from source, running tests, modifying core libraries, working on the Rust workspace, or onboarding as a contributor.
nox-py-dev
by elodin-sysContribute to the Elodin Python SDK (nox-py). Use when editing PyO3 bindings in libs/nox-py/, adding new Python API surface, modifying the JAX integration, working on component/system compilation, or changing the Python package in python/elodin/.
elodin-tracy
by elodin-sysProfile Elodin with Tracy. Use when profiling the editor, simulation, building with tracy features, capturing traces, analyzing performance, or adding custom instrumentation.
elodin-simulation
by elodin-sysCreate and modify physics simulations using the Elodin Python SDK. Use when writing or editing simulation Python files, defining components or systems, spawning entities, configuring 6DOF physics, setting up visualization, or integrating with SITL/HITL workflows.
elodin-nix
by elodin-sysWork with the Nix development environment and NixOS configurations in Elodin. Use when troubleshooting nix develop, modifying flake.nix, adding Nix packages, setting up OrbStack VMs for Linux builds on macOS, or developing Aleph NixOS modules.
elodin-editor-dev
by elodin-sysContribute to the Elodin Editor, the 3D viewer and graphing tool. Use when editing files in libs/elodin-editor/ or apps/elodin/, working on the Bevy/Egui UI, modifying viewport rendering, telemetry graphs, video streaming, KDL schematics, or the command palette.
bevy
by elodin-sysTips for working with a Bevy application
elodin-aleph
by elodin-sysDeploy and configure AlephOS on flight computers, write flight software services, and manage NixOS modules for the Aleph platform. Use when working with aleph/, deploying to Jetson Orin hardware, writing NixOS modules, flashing firmware, or composing a flight software stack.
elodin-cranelift
by elodin-sysWork with the Cranelift JIT MLIR backend. Use when modifying libs/cranelift-mlir/, adding new StableHLO ops, debugging simulation correctness issues, running the checkpoint diagnostic tool, or working on the pointer-ABI tensor runtime.
elodin-db
by elodin-sysWork with Elodin-DB, the time-series telemetry database. Use when running elodin-db, writing client integrations (C, C++, Rust, Python), configuring replication/follow mode, querying data via the Lua REPL, or connecting the Elodin Editor to a database.
gltf-asset-optimization
by elodin-sysReduce the size of glTF/GLB 3D assets to cut Git LFS bandwidth/storage while keeping them loadable by the editor's Bevy 0.18 glTF loader. Use when shrinking .glb/.gltf files in assets/, addressing LFS quota/bandwidth, or when a model is too large. Produces plain glTF 2.0 (no Draco/meshopt/quantization, which Bevy cannot read).
ai-skybox-development
by elodin-sysGuides development on the bevy_ai_skybox crate. Use when changing skybox generation, GPU equirect-to-cubemap conversion, GPU readback persistence, Bevy render-world wiring, examples, docs, or tests in this repository.
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.