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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charlie
by pinkpixel-devYour AI CFO for bootstrapped startups, named after Charlie Munger who embodied the principle that capital discipline is a competitive advantage. Provides financial frameworks for cash management, runway calculations, unit economics (LTV:CAC), capital allocation, hiring ROI, burn rate analysis, working capital optimization, and forecasting. Use for questions like "should we make this hire?", "how much runway do we need?", "what metrics should I track?", "how do I forecast revenue?", or any strategic financial decision at a self-funded company.
fal-tryon
by pinkpixel-devVirtual try-on — see how clothes look on a person. Use when the user requests "Try on clothes", "Virtual try-on", "How does this look on me", "Fashion try-on", "Garment transfer".
gpui-builder
by pinkpixel-devComplete guide for building GPU-accelerated desktop UI applications in Rust using GPUI — the hybrid immediate+retained mode framework powering the Zed editor. ALWAYS use this skill when the user mentions GPUI, gpui.rs, Zed UI framework, building a Rust GUI with GPU acceleration, or wants to create desktop apps in Rust with a Tailwind-like styling API. Also trigger when the user asks about Entities, Views, or Elements in a Rust GUI context, or wants to implement keyboard actions, async UI, custom canvas drawing, or testing in GPUI. If the user says "build me a Rust app with GPUI", "I want to use gpui", "how do I make a window in Rust", or asks about Zed's rendering architecture, use this skill immediately. Covers: architecture (Entities/Views/Elements), styling API, event/action system, async integration, custom canvas, testing, packaging, and comparison with iced/egui/slint.
onchain-pay-open-api
by pinkpixel-devBinance Onchain Pay enables users to buy cryptocurrency with fiat (e.g., EUR, USD) or send existing crypto from their Binance account directly to any external on-chain wallet address in a single flow—no manual withdrawal needed. Enables partners to integrate crypto buying services: - payment-method-list: Get available payment methods (Card, P2P, Google Pay, Apple Pay, etc.) with limits for a fiat/crypto pair - trading-pairs: List all supported fiat currencies and cryptocurrencies - estimated-quote: Get real-time price quote including exchange rate, fees, and estimated crypto amount - pre-order: Create a buy order and get redirect URL to Binance payment flow - order: Query order status and details (processing, completed, failed, etc.) - crypto-network: Get supported blockchain networks with withdraw fees and limits - p2p/trading-pairs: List P2P-specific trading pairs
album-conceptualizer
by pinkpixel-devDesigns album concepts, tracklist architecture, and thematic planning through 7 structured phases. Use when planning a new album or reworking an existing album concept.
album-art-director
by pinkpixel-devCreates visual concepts for album artwork and generates AI art prompts. Use during planning for concept discussion, or after all tracks are Final for actual artwork generation.
genre-creator
by pinkpixel-devCreate new genre documentation files for the bitwize-music genre library. Use when the user wants to add a genre, says "/genre-creator", "neues Genre erstellen", "Genre hinzufuegen", "add genre", or asks to create genre documentation. Takes a genre name as argument.
about
by pinkpixel-devProvides information about the bitwize-music plugin, its version, and its creator. Use when the user asks about the plugin, its purpose, version, or capabilities.
analyzing-linux-audit-logs-for-intrusion
by pinkpixel-devUses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.
charm-bracelet-tui
by pinkpixel-devComplete guide for building beautiful, interactive Go TUI (terminal UI) applications using the Charm Bracelet ecosystem. ALWAYS use this skill when the user mentions: bubbletea, bubbles, lipgloss, glamour, huh, harmonica, bubblezone, ntcharts, TUI in Go, terminal user interface in Go, terminal app with Go, CLI with interactive UI, charm.sh, charm.land, or wants to build any kind of interactive terminal application. Also trigger when building Go CLI tools that need forms, menus, progress bars, spinners, charts, markdown rendering, styled output, or mouse support. This covers the full stack from framework to styling to widgets to animations.
changelog-update
by pinkpixel-devDraft a new CHANGELOG.md entry for marzipan. Reads the current version and existing changelog format, then generates a properly-structured entry for the changes you describe.
remotion-best-practices
by pinkpixel-devBest practices for Remotion - Video creation in React
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.