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...
wind-site-assessment
by SpectrAI-InitiativeAssess wind energy potential and perform site analysis using atmospheric science calculations.
epd-parser
by AlpacaLabsLLCParse EPD (Environmental Product Declaration) PDF documents to extract structured environmental impact data — GWP, life cycle stages, certifications, and compliance metrics.
epd-research
by AlpacaLabsLLCSearch for EPDs by product category, CSI division, or material type. Finds EPDs from EC3, program operator registries, and manufacturer sites.
sustainability-design
by TibsfoxDesigning interventions, systems, and policies for sustainability. Covers renewable energy (solar, wind, hydro, geothermal), sustainable agriculture (agroecology, permaculture, integrated pest management), conservation strategies (protected areas, corridors, rewilding), lifecycle analysis, circular economy principles, and policy instruments (carbon pricing, cap-and-trade, regulation). Use when evaluating or designing interventions that aim to reduce environmental impact while maintaining human well-being.
conducting-environmental-impact-assessments
by CaseMarkEvaluates environmental compliance requirements with permitting risk, mitigation obligations, and ESG assessment for infrastructure investments. Use when assessing environmental risk, evaluating permitting timelines, or analyzing environmental compliance.
co2-carbon-footprint
by majiayu000Calculate and track CO2 emissions and carbon footprint for construction projects.
environmental-monitoring
by majiayu000Monitor environmental conditions on construction sites. Track air quality, noise levels, vibration, dust, and weather to ensure compliance and worker safety.
analyzing-water-and-waste-infrastructure
by lev-osEvaluates water treatment, waste management, and environmental services assets with regulatory compliance and growth drivers. Use when analyzing water infrastructure, evaluating waste assets, or assessing utility investments.
modeling-renewable-resource-yields
by lev-osBuilds renewable energy yield models with resource assessment, capacity factor analysis, and P50/P90 production estimates. Use when modeling wind/solar yields, analyzing resource data, or evaluating production uncertainty.
renewable-energy-expert
by luokai0Expert-level renewable energy covering solar, wind, hydro, geothermal, biomass, energy storage integration, grid integration, and renewable energy policy.
water-treatment-expert
by luokai0Expert-level water treatment covering drinking water treatment, wastewater treatment, industrial water systems, membrane processes, and water reuse.
energize-denver
by mbcoalsonUse when working with Denver's Energize Denver Article XIV building performance regulations. Provides compliance requirements, pathways, deadlines, penalties, MAI production efficiency metrics, benchmarking rules, and performance targets for commercial and multifamily buildings in Denver. Use when the user mentions Energize Denver, Denver Article XIV, MAI buildings, compliance pathways, performance targets, or Denver building performance requirements.
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.