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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lunco-ui
by LunCoSimLunCoSim UI architecture and panel implementation patterns. Use this skill whenever working on any user interface for the LunCoSim solar system simulation — adding panels, building dashboards, creating inspectors, spawning UI, telemetry displays, docking layouts, themes, or anything involving egui, bevy_workbench, or WorkbenchPanel. Also use when the user mentions CommandMessage, WidgetSystem, or 3D world-space UI. Even if the request seems simple (like "add a button"), use this skill because the panel registration and command patterns are project-specific and not obvious from Bevy alone.
lunco-theme
by LunCoSimLunCoSim's centralised theming system. Use this skill whenever you are about to write, touch, or review UI code that involves a color, spacing value, rounding, or egui visual style — in any panel, overlay, widget, gizmo label, or diagram. Trigger on any `Color32::from_rgb`, hex color, `ui.style_mut()`, `visuals.*`, `ctx.set_visuals`, "dark mode", "light mode", "accent color", "highlight", palette tweak, mention of Catppuccin, or work on a typed block-diagram editor (wire colours, class-kind badges). Also trigger when adding a new panel that needs colors, or when the user asks to "restyle", "retheme", or "make it match". The rules here are project-specific — defaults from egui or Bevy alone will lead you to hard-code colors, which violates the Tunability Mandate.
mission-operations-manager
by LunCoSimManage space mission operations, including telemetry & command definitions, pass planning, anomaly resolution, and procedure development. Use this skill when the user asks to "plan a pass," "define telemetry," "handle an anomaly," "create a command sequence," "write an operations procedure," or manage mission phases (LEOP, Commissioning, Routine).
requirements-manager
by LunCoSimManage space engineering requirements in a human-readable, trackable format. Use this skill whenever the user wants to define, update, trace, or review system requirements, component specifications, verification criteria, mass/power budgets as requirements, or perform traceability audits. Trigger for "requirement," "specification," "shall statement," "traceability," or "requirements review."
hazard-analysis
by LunCoSimPerform top-down Hazard Analysis (HA) and System Safety assessments. Use this skill to identify hazards, define safety controls (inhibits), trace them to requirements, and integrate with reliability FMECA. Trigger this for "hazard report," "safety analysis," "risk index," "inhibit design," or "system safety."
reliability-assessment
by LunCoSimPerform reliability, radiation risk, and failure mode assessments for space missions. Use this skill to calculate mission life probability, TID exposure, FMECA, and parts derating. This skill provides the bottom-up failure data used as "Causes" in hazard-analysis. Trigger for "FMECA," "reliability," "radiation," "TID," "single event effects," or "parts screening."
communications-assessment
by LunCoSimPerform RF link budget analysis, data volume assessments, and communication architecture sizing. Use this skill to calculate SNR, link margins, required antenna gains, data throughput, and ground station requirements. Trigger this for "link budget," "downlink rate," "RF analysis," "antenna sizing," "ground station," or "data budget."
structural-assessment
by LunCoSimPerform structural and mass properties analysis for spacecraft. Use this skill to calculate Center of Gravity (CG), Moments of Inertia (MOI), Margins of Safety under launch loads, and fundamental frequency estimates. Trigger this for "mass properties," "structural margin," "load analysis," "CG calculation," or "natural frequency."
propulsion-assessment
by LunCoSimPerform propulsion subsystem sizing and propellant mass estimation. Use this skill to select engine types, size propellant tanks, calculate burn durations, and evaluate electric vs chemical propulsion trades. Trigger this for "propellant sizing," "engine selection," "tank sizing," "burn duration," "electric propulsion," or "thrust-to-weight."
reusability-analysis
by LunCoSimAnalyze reusability of launch vehicles and spacecraft systems. Use this skill to size recovery hardware, estimate refurbishment costs, model reuse degradation, and calculate flight-rate economics. Trigger for "reusability," "landing propellant," "recovery system," "refurbishment," "turnaround time," "flight rate economics," "booster recovery," or "reuse degradation."
systems-engineering-assessment
by LunCoSimPerform integrated systems engineering assessments. Use this skill to generate and integrate Mass, Power, and Link budgets from domain skill outputs. Use this as the top-level integrator, budget arbitrator, and conflict resolver. Trigger this when the user asks for a "mission sizing," "budget update," "technical assessment," or when budgets from different subsystems need reconciliation.
thermal-assessment
by LunCoSimPerform detailed thermal analysis for spacecraft and rovers across various celestial environments. Use this skill to calculate heat balance, size radiators, determine MLI requirements, predict nodal temperatures, and plan thermal hardware. Trigger this for "thermal modeling," "radiator sizing," "temperature prediction," "heater sizing," or "thermal control."
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.