Orquestre um conselho de planejamento CLI configurável e com múltiplos membros (Codex, Claude Code, Gemini, OpenCode ou personalizado) para produzir planos de implementação independentes, anonimizá-los e randomizá-los, depois julgá-los e mesclá-los em um plano final. Use quando precisar de um fluxo de trabalho de planejamento robusto e resistente a viéses, saídas JSON estruturadas, novas tentativas e tratamento de falhas em vários agentes CLI.
O am-will/llm-council é uma skill para agentes de IA que orquestra um conselho de planejamento CLI com múltiplos agentes de linguagem (como Codex, Claude Code, Gemini, OpenCode ou personalizados). Ela permite gerar planos de implementação independentes, anonimizar e randomizar esses planos, e então julgá-los e mesclá-los em um plano final, promovendo um fluxo de trabalho de planejamento robusto e resistente a viéses. A skill é particularmente útil para desenvolvedores que precisam de saídas JSON estruturadas, suporte a novas tentativas e tratamento de falhas ao coordenar múltiplos agentes CLI. Ela funciona definindo um conselho com membros configurados, executando-os em paralelo ou sequencialmente, coletando seus planos, realizando uma rodada de julgamento anônimo e, por fim, gerando um plano unificado e aprimorado. Além disso, oferece recursos como anonimização de conteúdo para evitar viés de autor, aleatorização na ordem de apresentação e merge inteligente dos melhores elementos de cada plano, garantindo um resultado de alta qualidade e sem influências indevidas.
npx skills add https://github.com/am-will/codex-skills/tree/main/skills/llm-councilInstalled? Explore more Produtividade e Colaboração skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
Orquestre um conselho de planejamento CLI configurável e com múltiplos membros (Codex, Claude Code, Gemini, OpenCode ou personalizado) para produzir planos de implementação independentes, anonimizá-los e randomizá-los, depois julgá-los e mesclá-los em um plano final. Use quando precisar de um fluxo de trabalho de planejamento robusto e resistente a viéses, saídas JSON estruturadas, novas tentativas e tratamento de falhas em vários agentes CLI.
This repo contains 17 individual skills — each has its own dedicated page.
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.
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.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.