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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build123d-cad-mechanical
by baibai2013build123d Python CAD 机械建模子技能。零件设计 → 装配爆炸 → 关节安装 → 制造工艺 → FK/IK/步态 全链路。 融合 Dave Cowden「像机械师思考」与 Peter Corke「Learn by doing」哲学。 触发词：build123d、CAD建模、做一个零件、参数化、导出 STEP、装配、爆炸图、Joint、关节、 仿真、FK、IK、步态、3D打印工艺、CNC、激光切割、surface modeling、loft、sweep。 本子技能不做：URDF/SRDF/SDF 描述(→ urdf/srdf/sdf)、网页预览(→ viewer)、切片/钣金报价(→ gcode/sendcutsend)。
robot-dog-digital-twin
by baibai2013Mechanical dog digital-twin orchestrator. Use this skill whenever the user mentions robot dog digital twins, virtual prototypes, design gates before physical builds, multi-domain validation across CAD/PCB/circuit/simulation/gait, design scoring, failure reports, or iteration plans. This skill only reads file artifacts from other build123d-cad subskills, runs deterministic gates and scoring, and produces design_score, gate_report, failure_report, and next_iteration_plan outputs.
build123d-cad-parts-catalog
by baibai2013build123d 标准件目录子技能。在自己建模之前先回答「这个件别人画过没?能直接用吗?」 覆盖：① 常用标准件索引(轴承 608/625/MR/skf、M2-M8 螺丝/螺母/垫片、SG90/MG996R 舵机、 NEMA8/14/17 步进、O 型圈、卡簧、销轴);② STEP 来源策略与 license 红线 (McMaster-Carr / GrabCAD / TraceParts / 厂商官网);③ 与 build123d-parts-lib 仓库对接 (parts_lib: 字段 → make_xxx() 工厂函数);④ 落盘 STEP → handoff 给 mechanical 装配。 触发词:找现成件、标准件、STEP 下载、McMaster、GrabCAD、TraceParts、608 轴承、 M3 螺丝、SG90、NEMA17、O 型圈、卡簧、screw、bearing、fastener、part library。 本子技能不做:从零参数化建模(→ mechanical)、URDF(→ urdf)、网页预览(→ viewer)。
motion-control
by baibai2013Executable robot-dog motion-control MVP for leg IK, gait phase generation, trajectory contracts, controller parameters, and simulation/firmware handoff. Use this skill when the user asks for FK/IK, inverse kinematics, gait generator, trot/walk/bound trajectories, controller parameters, motion-control code, or a trajectory to feed simulation, MuJoCo, or firmware.
firmware
by baibai2013Robot-dog firmware dry-run planning and validation skill for MCU target, motor-control loop, CAN protocol, safety state machine, calibration contract, build manifest, and hardware bring-up gates. Use this skill when the user asks for embedded firmware, motor firmware, FOC loop, CAN frames, calibration, firmware project scaffolding, or safe pre-flash validation.
electronics-bom
by baibai2013Electronics BOM and robot-dog component selection MVP. Use this skill when the user asks for electronic BOM, MCU/driver/encoder/power/connector selection, JLCPCB/LCSC-ready part candidates, curated robot electronics library, BOM rationale, availability checks, or component choices to feed pcb, circuit simulation, firmware, and digital-twin gates.
mujoco-simulation
by baibai2013MuJoCo-oriented robot-dog dynamics validation, MJCF scenario contracts, contact, terrain, actuator, stability, torque, slip, and disturbance checks for virtual prototypes. Use this skill when the user asks for MuJoCo, MJCF, high-fidelity legged-robot simulation, contact/friction validation, slope/step/drop/push scenarios, or serious gait simulation beyond PyBullet smoke tests.
integration
by baibai2013Robot-dog integration, bring-up, HIL planning, prototype readiness, hardware gate, assembly test, first power-on checklist, data capture, and physical test safety validation. Use this skill when the user asks whether a prototype is ready for assembly, power-on, HIL, bring-up, hardware testing, or real-world data collection after digital-twin gates.
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.