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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media-processing
by samhvw8Video/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.
mise-expert
by samhvw8Mise development environment manager (asdf + direnv + make replacement). Capabilities: tool version management (node, python, go, ruby, rust), environment variables, task runners, project-local configs, backend selection (aqua, github, core, cargo, npm, pipx), security verification (cosign, SLSA, GitHub attestations, minisign). Actions: install, manage, configure, run tools/tasks with mise, troubleshoot attestation failures, migrate backends. Keywords: mise, mise.toml, tool version, runtime version, node, python, go, ruby, rust, asdf, direnv, task runner, environment variables, version manager, .tool-versions, mise install, mise use, mise run, mise tasks, project config, global config, aqua backend, github backend, ubi deprecated, MISE_AQUA_COSIGN, MISE_AQUA_GITHUB_ATTESTATIONS, attestation verification failed, cosign, SLSA, uv astral-sh. Use when: installing runtime versions, managing tool versions, setting up dev environments, creating task runners, replacing asdf/direnv/make, configuring project-local tool
payment-integration
by samhvw8Payment gateway integration. Providers: SePay (Vietnamese: VietQR, bank transfer, cards), Polar (global SaaS: subscriptions, usage-based billing). SDKs: Node.js, PHP, Python, Go, Laravel, Next.js. Capabilities: checkout flows, subscription management, webhooks, QR code generation, benefit automation, tax compliance. Actions: integrate, implement, configure, handle payments/subscriptions/webhooks. Keywords: payment gateway, SePay, Polar, VietQR, bank transfer, subscription, usage-based billing, checkout, webhook, QR code, API key, OAuth2, product management, customer portal, tax compliance, MoR, recurring payment, invoice. Use when: integrating payment processing, implementing checkout, managing subscriptions, handling payment webhooks, generating payment QR codes, building billing systems.
planning
by samhvw8Universal planning for technical and non-technical projects. Domains: software implementation, business, personal, creative, academic, events. Capabilities: feature planning, system architecture, goal setting, milestone planning, requirement breakdown, trade-off analysis, resource allocation, risk assessment. Actions: plan, architect, design, evaluate, breakdown, structure projects. Keywords: implementation plan, technical design, architecture, roadmap, project plan, strategy, goal setting, milestones, timeline, action plan, SMART goals, sprint planning, task breakdown, OKRs. Use when: planning features, designing architecture, creating roadmaps, setting goals, organizing projects, breaking down requirements.
forager
by samhvw8BETA iterative research with goal-directed steering, topic expansion, and saturation detection. Synthesizes 6 human research methodologies (information foraging, berry picking, Kuhlthau ISP, intelligence cycle, ACH, grounded theory saturation) into one loop. ONLY invoke when: user says 'forager' or 'beta research', OR lead-researcher detects open-ended/deep research need and asks user first. NOT for quick lookups, single comparisons, or fact-checking — use lead-researcher for those. NOT a replacement for lead-researcher — it's a separate deeper methodology.
chrome-devtools
by samhvw8Browser automation via Puppeteer CLI scripts (JSON output). Capabilities: screenshots, PDF generation, web scraping, form automation, network monitoring, performance profiling, JavaScript debugging, headless browsing. Actions: screenshot, scrape, automate, test, profile, monitor, debug browser. Keywords: Puppeteer, headless Chrome, screenshot, PDF, web scraping, form fill, click, navigate, network traffic, performance audit, Lighthouse, console logs, DOM manipulation, element selector, wait, scroll, automation script. Use when: taking screenshots, generating PDFs from web, scraping websites, automating form submissions, monitoring network requests, profiling page performance, debugging JavaScript, testing web UIs.
browser-history
by samhvw8Search local browser history. Use when user asks about visited pages, forgotten URLs, or time spent on sites.
claude-ecosystem
by samhvw8ALWAYS invoke this skill when the user asks about Claude Code features, configuration, extensibility, or project setup. ALWAYS invoke when the user wants to create, improve, or refine a Claude Agent Skill — including 'make this a skill', 'turn this into a skill', 'create a skill for X', or asks about SKILL.md authoring, frontmatter, description optimization, progressive disclosure, or triggering strategy. Modules: CLI tool (setup, slash commands, MCP servers, hooks, plugins, CI/CD), extensibility (agents, skills, output styles), CLAUDE.md (project instructions, optimization). Keywords: Claude Code, skill, SKILL.md, agent, hook, plugin, MCP, CLAUDE.md, skill architecture, description optimization, progressive disclosure. For skill body content quality (soul, tensions, mental models), also invoke prompt-architect. Do NOT use for general prompt engineering without a Claude Code context.
refactoring-expert
by samhvw8Systematic code refactoring following Martin Fowler's catalog. Methodologies: characterization tests, Red-Green-Refactor, incremental transformation. Capabilities: SOLID compliance, DRY cleanup, code smell detection, complexity reduction, legacy modernization, design patterns, functional programming patterns. Actions: refactor, extract, inline, rename, move, simplify code. Keywords: refactor, SOLID, DRY, code smell, complexity, extract method, inline, rename, move, clean code, technical debt, legacy code, design pattern, characterization test, Red-Green-Refactor, functional programming, higher-order function, immutability, pure function, composition, currying, side effects. Use when: improving code quality, reducing technical debt, applying SOLID principles, fixing DRY violations, removing code smells, modernizing legacy code, applying design patterns.
payment-integration
by samhvw8Payment gateway integration. Providers: SePay (Vietnamese: VietQR, bank transfer, cards), Polar (global SaaS: subscriptions, usage-based billing). SDKs: Node.js, PHP, Python, Go, Laravel, Next.js. Capabilities: checkout flows, subscription management, webhooks, QR code generation, benefit automation, tax compliance. Actions: integrate, implement, configure, handle payments/subscriptions/webhooks. Keywords: payment gateway, SePay, Polar, VietQR, bank transfer, subscription, usage-based billing, checkout, webhook, QR code, API key, OAuth2, product management, customer portal, tax compliance, MoR, recurring payment, invoice. Use when: integrating payment processing, implementing checkout, managing subscriptions, handling payment webhooks, generating payment QR codes, building billing systems.
ai-multimodal
by samhvw8Multimodal AI processing via Google Gemini API (2M tokens context). Capabilities: audio (transcription, 9.5hr max, summarization, music analysis), images (captioning, OCR, object detection, segmentation, visual Q&A), video (scene detection, 6hr max, YouTube URLs, temporal analysis), documents (PDF extraction, tables, forms, charts), image generation (text-to-image, editing). Actions: transcribe, analyze, extract, caption, detect, segment, generate from media. Keywords: Gemini API, audio transcription, image captioning, OCR, object detection, video analysis, PDF extraction, text-to-image, multimodal, speech recognition, visual Q&A, scene detection, YouTube transcription, table extraction, form processing, image generation, Imagen. Use when: transcribing audio/video, analyzing images/screenshots, extracting data from PDFs, processing YouTube videos, generating images from text, implementing multimodal AI features.
docx
by samhvw8Word document processing. Format: .docx (ZIP/XML structure). Capabilities: create documents, edit content, tracked changes, comments, formatting preservation, text extraction, styles, headers/footers, tables, images. Actions: create, edit, analyze, extract from Word documents. Keywords: Word, docx, document, tracked changes, comments, formatting, styles, headers, footers, tables, images, paragraphs, text extraction, template, mail merge, revision history, document comparison. Use when: creating Word documents, editing docx files, working with tracked changes, adding comments, extracting document content, preserving document formatting.
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.