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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vendor-competitive-analysis
by DavidsonCollegeCompare a software vendor against its competitors for a higher education procurement decision. Use this skill when someone asks how a vendor stacks up against alternatives, wants a feature comparison matrix, needs to understand competitive positioning, or is evaluating multiple products in the same category. Trigger on 'compare vendors', 'alternatives to', 'competitor analysis', 'feature comparison', 'versus', 'vs', 'how does X compare to Y', 'competitive landscape', or any request to evaluate a vendor relative to its market competitors.
vendor-cost-analysis
by DavidsonCollegeAnalyze the total cost of ownership, pricing model, and budget impact of a software vendor for a higher education institution. Use this skill when someone asks about vendor pricing, licensing costs, educational discounts, ROI, TCO, budget planning for software, or cost comparisons between vendors. Trigger on 'vendor pricing', 'how much does it cost', 'educational discount', 'TCO analysis', 'budget impact', 'licensing model', 'cost comparison', 'ROI', or any request to understand the financial implications of purchasing a software product.
vendor-reputation
by DavidsonCollegeResearch and assess a software vendor's market reputation, financial health, and customer satisfaction for a higher education procurement decision. Use this skill when someone asks about a vendor's reputation, track record, customer reviews, analyst ratings, company financials, or general market standing. Trigger on 'vendor reputation', 'company background', 'customer reviews', 'vendor viability', 'market position', 'Gartner rating', 'G2 reviews', or any request to understand whether a vendor is trustworthy and well-regarded before purchasing.
vendor-research-coordinator
by DavidsonCollegeOrchestrate a comprehensive, multi-perspective vendor research process for a higher education institution. Use this skill whenever someone asks to evaluate, research, or assess a software vendor for potential purchase. This is the master coordinator that launches parallel research agents across security, reputation, cost, legal, competitive, higher ed fit, and build feasibility dimensions, then synthesizes findings into a professional Vendor Research Brief document. Trigger on 'evaluate a vendor', 'research this product', 'vendor assessment', 'should we buy', 'procurement research', 'vendor due diligence', 'vendor research brief', or any request to comprehensively evaluate a technology vendor before purchasing. Always use this skill instead of doing ad-hoc research — it ensures complete coverage.
vendor-higher-ed-fit
by DavidsonCollegeAssess how well a software vendor fits the specific needs of a higher education institution, including academic workflows, seasonal patterns, compliance requirements, and peer institution adoption. Use this skill when someone asks whether a vendor is a good fit for higher ed, wants to know about college/university adoption, needs to understand academic-specific requirements, or is evaluating a product through an educational lens. Trigger on 'higher ed fit', 'university use', 'college adoption', 'academic requirements', 'EDUCAUSE', 'peer institutions', 'campus deployment', or any request to evaluate a vendor specifically for educational institution use.
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.