am-will/agent-browser
A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
npx skills add https://github.com/am-will/codex-skills/tree/main/skills/agent-browserA fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
This repo contains 17 individual skills — each has its own dedicated page.
Fetch up-to-date library documentation via Context7 CLI. Use PROACTIVELY when: (1) Working with ANY external library (React, Next.js, Supabase, etc.) (2) User asks about library APIs, patterns, or best practices (3) Implementing features that rely on third-party packages (4) Debugging library-specific issues (5) Need current documentation beyond training data cutoff (6) AND MOST IMPORTANTLY, when you are installing dependencies, libraries, or frameworks you should ALWAYS check the docs to see what the latest versions are. Do not rely on outdated knowledge. Always prefer this over guessing library APIs or using outdated knowledge.
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
Build responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.
Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions.
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents.
Route any website you need to visit through markdown.new by prefixing the URL. **WHEN TO USE:** - You would normally open a website link to read content (docs, blog posts, changelogs, GitHub issues, etc.) - You need a cleaner, Markdown-friendly view for copying notes or summarizing
Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq). Use whenever a task involves the OpenAI API (Responses, Chat Completions, Realtime, etc.), OpenAI SDKs, ChatGPT Apps SDK, Codex, MCP integrations, endpoint schemas, parameters, limits, or migrations and you need up-to-date official guidance.
Only to be triggered by explicit /parallel-task commands.
Only to be triggered by explicit /parallel-task-spark commands.
Use when user specfically says 'plan harder'.
Create comprehensive, phased implementation plans with sprints and atomic tasks. Use when user says: "make a plan", "create a plan", "plan this out", "plan the implementation", "help me plan", "design a plan", "draft a plan", "write a plan", "outline the steps", "break this down into tasks", "what's the plan for", or any similar planning request. Also triggers on explicit "/planner" or "/plan" commands.
Read and search GitHub repository documentation via gitmcp.io MCP service. **WHEN TO USE:** - User provides a GitHub URL - User mentions a specific repo in owner/repo format - User asks "what does this repo do?", "read the docs for X repo", or similar - User wants to search code or docs within a repo
Create and update Codex custom agents using standalone custom-agent TOML files.
Only to be triggered by explicit super-swarm-spark commands.
[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.
Writes failing tests first for test-driven development and hands off a strict implementation contract that requires agents to make those tests pass without weakening the tests. Use when users ask for test-first workflows, RED/GREEN cycles, or behavior-gating tasks with automated tests.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
WeChat-to-Codex bridge for running Codex app-server from chat, with threads, slash commands, approvals, agents, automation, uploads, and assistant records.
Multi-agent workflow system for Claude Code
Codex skill for cross-session engineering workflow, handoffs, acceptance, and repo coordination.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
A reproducible AWS HealthOmics private workflow that runs GPU-accelerated Parabricks DeepVariant (BAM to VCF), built to work around a silent 0-byte-VCF failure in the prebuilt Ready2Run pipeline. Includes a Claude Skill for AI-assisted use.
Data service for Agent: enables more efficient retrieval of information on news, companies (including stocks), public services (meetings, weather), products(e-commerce), AI models and etc.