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...
include-small-genset-in-pv-battery-hybrid-rather-than-100-batter
by BulloRossoInclude small genset in PV+battery hybrid rather than 100% battery for island RO systems. Use when: designing a renewable energy system for remote island desalination with solar resource available. Provides: size a 70/30 PV+genset hybrid (e.g. 70% renewable, 30% genset backup) instead of 100% PV+battery, using the genset to enable emergency CIP cycles and evening load coverage.
ground-tco-analysis-in-regional-reference-cases-with-published-d
by BulloRossoGround TCO analysis in regional reference cases with published data. Use when: analyzing water treatment costs for a new geographic scenario. Provides: immediately cite a nearby successful deployment (e.g., Bequia for Caribbean) with published capex, opex, and payback metrics; use it as an anchor to identify region-specific cost drivers.
roleplay-author
by BulloRossoCo-author a new roleplay scenario with an expert. Interview the expert about the persona, topic, hints with point values, and evaluation criteria, draft the JSON, and on confirmation write it to roleplay/<slug>.roleplay.json. Use when the expert invokes "Author a roleplay scenario" from the menu, asks "let me add a customer call to practice", "I want to script a difficult buyer", or equivalent. The scenarios authored here are consumed at runtime by the roleplay-engine skill.
draft-quotes
by BulloRossoAutomates RFQ intake via web form and email, calculates pricing from product definitions, and drafts HTML quote emails for sales review
validate-against-multiple-regulatory-standards-even-when-only-on
by BulloRossoValidate against multiple regulatory standards even when only one applies locally. Use when: A technology selection involves water quality standards or regulatory limits that vary by jurisdiction, and the local jurisdiction is outside the primary standard-setting region (e.g., EU, WHO). Provides: Evaluate the design against both the locally applicable standard AND the stricter international standard (EU, WHO). Document the comparison and make a deliberate choice about which to design to, even if only one is legally required..
deep-scientific-research
by BulloRossoDeep scientific research pipeline with subagent orchestration, parallel web search, and citation management. Use this skill whenever the user asks for a literature review, scientific research summary, evidence synthesis, systematic review, or any deep research task that requires searching multiple sources and producing a cited report. Also triggers on phrases like "research this topic", "find papers on", "what does the evidence say about", "literature review on", or "deep dive into".
document-creation
by BulloRossoAssemble a Word .docx by copying sections from one or more source PDF/Word documents in source/ into a target template in target/, applying per-section transformation instructions (translate, summarize, include/exclude images). Driven by source-target.sectionmappings.json. Trigger on "create the document", "build the target docx", "generate the document from the mappings", or when the user finishes the section-mapping dashboard.
browser-use
by BulloRossoBrowser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to 'open a website', 'fill out a form', 'click a button', 'take a screenshot', 'scrape data from a page', 'test this web app', 'login to a site', 'automate browser actions', or any task requiring programmatic web interaction.
document-search
by BulloRossoIndex and search documents using semantic search (RAG). Automatically indexes files in the project's documents/ folder and enables natural language document retrieval.
decision-support
by BulloRossoOrchestrates ontology-grounded decision support for Node.js applications using quadstore (RDF), ZeroMQ event-based condition monitoring, and an LLM reasoning layer. Use when the user wants to derive actionable decisions from a chat context, define conditions and actions on ontology entities, build or refine decision graphs, or export rule sets for the ZMQ execution layer.
validate-parameters-against-both-who-and-eu-standards-even-when
by BulloRossoValidate parameters against both WHO and EU standards even when targeting one jurisdiction. Use when: evaluating compliance for a water treatment design. Provides: check every critical parameter against both WHO and EU limits separately, even if deployment is outside the EU.
public-website
by BulloRossoGuides the agent to create professional public websites with React/MUI served from the project's /web subdirectory with hot-reloadable API endpoints
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.