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...
spatial-planning
by h30190Spatial planning for buildings: floor plan layout and archetypes, circulation design (corridors, stairs, elevators, escalators), core design and vertical service distribution, vertical stacking strategy, net-to-gross optimization, space efficiency metrics, wayfinding, and spatial legibility.
public-housing-nz
by thecolab-aiQuery New Zealand public and social housing open data from the Ministry of Housing and Urban Development (HUD) on data.govt.nz — public housing stock (Kainga Ora and community housing providers), social housing IRRS and market-rent tenancies, accommodation supplement recipients and weekly spend, and the Local Housing Statistics dashboard (housing affordability, rent burden, bonds, building consents, MSD benefit numbers, and the year-on-year change in the public/social housing register). Use when the task involves NZ public housing, the social housing register or wait-list, Kainga Ora, housing deprivation, accommodation supplement, housing affordability, or HUD housing statistics. No API key, login, or browser session required.
analyzing-just-transition
by lev-osEvaluates just transition implications of decarbonization with workforce impact and community assessment. Use when analyzing just transition, assessing workforce impacts, or evaluating community effects.
municipal-engineer
by HaibarakikuA licensed municipal engineer specializing in urban infrastructure, water distribution, stormwater management, and public facilities. Use when designing municipal water systems, stormwater networks, roads, or public works projects. Use when: municipal, infrastructure, public-works, stormwater, water-distribution.
urban-planner-analyst
by rysweetAnalyzes urban development through planning lens using zoning, land use, comprehensive planning, and transit-oriented development frameworks. Provides insights on spatial organization, infrastructure, sustainability, and livability. Use when: Urban development projects, zoning decisions, transportation planning, sustainability initiatives. Evaluates: Land use patterns, density, accessibility, environmental impact, community needs.
environment
by alistaircrollAccess environmental data for Montréal: public tree inventory (333,556 trees), air quality monitoring, green spaces, canopy coverage, water quality, and urban heat islands. / Accéder aux données environnementales de Montréal : inventaire des arbres publics (333 556 arbres), qualité de l'air, espaces verts, canopée, qualité de l'eau et îlots de chaleur.
zoning-analyst
by caishengold当需要对分区分析领域数据进行清洗、统计分析、趋势报告时使用。触发场景：土地分区/用地分析。当用户提到"分区分析"、"土地分区"、"用地分析"、"zoning"时应触发此技能。
parcel-zoning-research
by Elliot-SonesExtracts and normalizes parcel, zoning, Official Plan, and overlay facts from a previously discovered Ontario source bundle. This skill should be used when `source_bundle.json` exists and the pipeline needs a source-backed `normalized_data.json` for downstream analysis.
scb-housing-construction
by ashwinvisWorkflows and use cases for SCB Housing and Construction tables covering real estate, building permits, and construction statistics
crisis-detection-domain-knowledge
by DevWithFarazDomain expertise for crisis signals and Islamabad geography
city-state-simulation-engine
by DevWithFarazHow CityStateSimulator computes before/after metrics
systematic-review
by ehsansobhaniConduct a PRISMA-compliant systematic literature review for urban BEV fast-charging rollout planning research.
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.