381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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OneWave-AI
Showing 12 of 155 skills
OneWave-AI

job-application-optimizer

by OneWave-AI
star 188

Optimize job applications by tailoring resumes to job postings, generating customized cover letters, and preparing role-specific interview questions. Analyzes job descriptions to highlight relevant skills and experience. Use when users need to apply for jobs, customize resumes, or prepare for interviews.

navigation main article SKILL.md
schedule Updated 16 days ago
OneWave-AI

quota-setting-calculator

by OneWave-AI
star 188

Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

quiz-maker

by OneWave-AI
star 188

Create multiple choice, true/false, fill-in-blank, matching quizzes. Auto-generate plausible distractors. Instant grading with explanations.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

ramping-rep-tracker

by OneWave-AI
star 188

30/60/90/120 day ramp milestones. Activity expectations by week, certification progress, early warning indicators.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

prospect-panel-simulator

by OneWave-AI
star 188

Simulate a panel of your real prospects and buyers to pressure-test sales and marketing before it goes out — cold emails, pitch decks, landing pages, pricing pages, demo scripts, proposals. Each simulated prospect reacts in character, raises the objection they'd actually raise, and tells you whether they'd reply, book, or ghost. Use to de-risk outbound and messaging without burning real leads.

navigation main article SKILL.md
schedule Updated 19 days ago
OneWave-AI

sales-comp-plan-designer

by OneWave-AI
star 188

Base/variable split recommendations, accelerators, decelerators, quota retirement methods, SPIFs, cost of sales tracking.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

landing-page-optimizer

by OneWave-AI
star 188

Optimize landing pages for conversions, performance, and SEO. Use when improving landing pages, increasing conversions, or optimizing page performance.

navigation main article SKILL.md
schedule Updated 16 days ago
OneWave-AI

landing-page-copywriter

by OneWave-AI
star 188

Write high-converting landing page copy using proven frameworks like PAS (Problem-Agitate-Solution), AIDA, and StoryBrand. Creates headlines, value propositions, CTAs, and full page sections optimized for conversion. Use when users need landing page copy, sales page content, or marketing website text.

navigation main article SKILL.md
schedule Updated 16 days ago
OneWave-AI

bracket-predictor

by OneWave-AI
star 188

March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

game-strategy-simulator

by OneWave-AI
star 188

What-if scenario analyzer for sports. Play-calling recommendations, clock management, substitution patterns, risk/reward calculations.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

player-comparison-tool

by OneWave-AI
star 188

Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.

navigation main article SKILL.md
schedule Updated 8 months ago
OneWave-AI

scouting-report-builder

by OneWave-AI
star 188

Generate player and team scouting reports. Strengths, weaknesses, tendencies, film breakdown, matchup advantages.

navigation main article SKILL.md
schedule Updated 8 months ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.