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...
extract-design
by Manavarya09Extract the full design language from any website URL. Produces 8 output files including AI-optimized markdown, visual HTML preview, Tailwind config, React theme, shadcn/ui theme, Figma variables, W3C design tokens, and CSS variables. Also runs WCAG accessibility scoring. Use when user says 'extract design', 'get design system', 'design language', 'design tokens', 'what colors/fonts does this site use', or '/extract-design'.
cost-guardian
by Manavarya09Real-time cost tracking and budget management for Claude Code sessions. Shows current spend, burn rate, budget status, per-tool and per-model breakdown. Use when user says 'cost', 'budget', 'spending', 'how much', 'token usage', 'export', or '/cost-guardian'. Subcommands: budget, resume, reset, config, export, status.
cost-insights
by Manavarya09Advanced cost intelligence: efficiency scoring, waste detection, ROI metrics, model savings calculator, anomaly detection, and GitHub badge generation. Use when user says 'insights', 'efficiency', 'waste', 'roi', 'savings', 'anomaly', 'badge', or '/cost-insights'.
cost-estimate
by Manavarya09Estimate the cost of a task before starting it. Analyzes task complexity, predicts token usage, and compares cost across all Claude models. Use when user says 'estimate cost', 'how much will this cost', 'cost estimate', or '/cost-estimate'.
cost-report
by Manavarya09Generate detailed cost reports with charts and breakdowns. Shows daily/weekly/monthly spend, per-tool costs, per-branch costs, and trends. Use when user says 'cost report', 'spending report', 'usage report', 'show costs', 'branch costs', or '/cost-report'.
moldui-sync
by Manavarya09Apply pending visual edits from moldui to source code. Triggers: /moldui-sync, apply my moldui changes, sync visual edits, apply canvas edits
canvas
by Manavarya09Visual editor overlay for any running web app. Drag, resize, edit text, change styles in the browser — changes sync to source code. Triggers: /canvas, visual edit, drag drop editor, mold UI
tokenforge
by Manavarya09TokenForge — Full-stack LLM token optimization engine
checkpoint
by Manavarya09Cursor-style checkpoint & undo for Claude Code. Instantly revert to any previous state. Trigger: '/checkpoint', '/cp', 'undo changes', 'revert changes', 'go back to before', 'restore previous version', 'undo last prompt'.
city
by Manavarya09Visualize your current project as a 3D city in the browser. Files become buildings, folders become districts, bugs become fires. Use when user says 'city', 'visualize', '3d', 'show my code', or '/city'.
team-brain-onboard
by Manavarya09Generate onboarding context for new team members from team brain knowledge. Use when user says 'onboard', 'onboarding', 'new developer', 'getting started', or '/team-brain onboard'.
team-brain
by Manavarya09Shared team memory for Claude Code. Record lessons, decisions, and conventions that persist across sessions and team members. Use when user says 'team brain', 'learn', 'decide', 'convention', 'recall', 'team memory', or '/team-brain'. Subcommands: learn, decide, convention, recall, status, init.
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.