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.
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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.
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安装前可以查看交给 AI 代理的指令内容。
$XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json). If none exists, tell the user to run ./setup.sh to configure or update agents.python3 scripts/llm_council.py run --spec /path/to/spec.json to run the council../llm-council/runs/<timestamp> relative to the current working directory.python3 scripts/llm_council.py configure (writes $XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json).judge.md and final-plan.md.final-plan.md are confirmed saved; keep the session open during that interval to avoid closing the interface. If you yield while the Council is running, the session will be terminated and you will FAIL to complete the task. The user will escape out when they are ready or after the 30 minutes have elapsed.
Use agents.planners to define any number of planning agents, and optionally agents.judge to override the judge.
If agents.judge is omitted, the first planner config is reused as the judge.
If agents is omitted in the task spec, the CLI will use the user config file when present, otherwise it falls back to the default council.
Example with multiple OpenCode models:
{
"task": "Describe the change request here.",
"agents": {
"planners": [
{ "name": "codex", "kind": "codex", "model": "gpt-5.2-codex", "reasoning_effort": "xhigh" },
{ "name": "claude-opus", "kind": "claude", "model": "opus" },
{ "name": "opencode-claude", "kind": "opencode", "model": "anthropic/claude-sonnet-4-5" },
{ "name": "opencode-gpt", "kind": "opencode", "model": "openai/gpt-4.1" }
],
"judge": { "name": "codex-judge", "kind": "codex", "model": "gpt-5.2-codex" }
}
}
Custom commands (stdin prompt) can be used by setting kind to custom and providing command and prompt_mode (stdin or arg).
Use extra_args to append additional CLI flags for any agent.
See references/task-spec.example.json for a full copy/paste example.
references/architecture.mdreferences/prompts.mdreferences/templates/*.mdreferences/cli-notes.mdfinal-plan.md are saved; keep the session open during that interval to avoid closing the interface.还没有评价。欢迎分享你的使用感受。
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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.
中文简介正在准备中。当前显示原文说明。
Create or update Codex hooks for global or project scope by asking for scope and hook type when ambiguous, then writing the hook config, scripts, and any required feature flag config.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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Build, transform, audit, and migrate prompts for GPT-5.6 Sol using outcome-first contracts, explicit success criteria, evidence and tool rules, approval boundaries, output requirements, and stop conditions. Use when someone asks for a GPT-5.6 prompt, wants an existing prompt optimized for GPT-5.6, or needs a prompt stack adapted from an older model.
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