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...
open-loop-lut-recollection
by Idate96Recollect bounded-joint open-loop current LUT data on the robot machine using one bag per amplitude, asymmetric start poses, dense low-speed knee filling, and immediate per-point analysis. Use when recollecting or repairing `J_TELE` / `J_EE_PITCH` LUT points or when `mole_sysid_tune_lut` rejects edge amplitudes.
newton-nav-stack-test
by Idate96Validate the Newton + ROS Nav2 driving stack in a clean tmux session after bringup. Use when the user wants a repeatable navigation check in Newton sim, including health checks for the bridge/model/drive path and the lateral-shift golden test.
rl-newton
by Idate96Entry point for Moleworks Newton RL work. Use when working on Newton training, shared-turn experiments, local smoke tests, cluster launches, benchmark interpretation, or experiment ledgers. Routes to narrower Newton skills for cluster ops, benchmarking, ROS parity, and long-horizon orchestration.
rl-newton-cluster-ops
by Idate96Submit, monitor, sync, and ledger Moleworks Newton RL runs on Euler or Brev. Use when preparing a smoke test, launching shared-turn jobs, checking `squeue`/`sacct`/Slurm logs, syncing one run, or updating `docs/experiments`.
newton-sim-ros-startup
by Idate96Start or restart the Moleworks ROS2 stack using the Newton simulator in the default moleworks_ros runtime shell, assuming the current shell is already inside the target container unless the user says otherwise. Use when you need a clean tmux layout for Newton bridge, robot/TF/RViz, perception (elevation + excavation mapping), optional Foxglove bridge, an isolated bridge-only validation stack on a specific ROS domain, or Terra failure capture and resume from saved checkpoints in Newton simulation, all with use_sim_time:=true.
rl-newton-benchmark
by Idate96Benchmark and analyze Moleworks Newton RL checkpoints. Use when benchmarking analytic cabin or shared-turn checkpoints, collecting or replaying terrain banks, verifying a saved bank, comparing fresh vs carved terrain, or summarizing benchmark JSON outputs.
newton-ros-parity
by Idate96Validate or replay Moleworks Newton observations through `moleworks_ros`. Use when bringing up the ROS parity stack, checking `/clock` and TF, debugging controller-facing terrain topics, replaying Dig3D-style comparisons, or cleaning up stale ROS/Newton processes after parity work.
terra-foundation-execution
by Idate96Execute packaged Moleworks Terra flat-foundation plans in Newton simulation, especially flat_foundation_depth_0p5 end-to-end with Nav2 navigation, async checkpoints, workspace-planner target recomputation, post-dump stall measurement, checkpoint resume, and failure-state capture. Use after newton-sim-ros-startup has established the ROS/Newton runtime.
dig-controllers
by Idate96Start Moleworks dig controllers (dig_3d, dig_newton, dig_ee, etc.) in a split tmux window: one pane runs the controller launch, the other pane runs lifecycle helpers and shows the `ros2 action send_goal` command. Use when the base stack is already running and you want to add or restart only a dig controller.
dig-bag-recording
by Idate96Start fast split rosbag recording for Mole dig/newton runs using the canonical `rosbag_record.launch.py` workflow. Use when you want separate bags for sensors, state, commands, lidar, camera (compressed image topics), elevation_map, and Dig3D special observations during digging experiments.
workspace-planner-debug
by Idate96Debug Moleworks workspace planner Newton/ROS runs with per-action planner GridMaps, predicted-vs-executed scoop analysis, Terra checkpoint replay of failed or high-discrepancy scoops, and live-run inspection artifacts.
student-onboarding
by Idate96Onboard a new RSL student project across the student spreadsheet, shared Google Drive project folders, and follow-up access/admin requests. Use when Codex must add a new student entry, create the project folder from the template, place the grading sheet correctly, or follow the ETH/RSL student-start checklist.
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.