moot-court-ai — ИИ-симулятор китайского гражданского судопроизводства

--- name: moot-court-ai description: Simulate a full Chinese civil court hearing with 4 role-based agents (clerk, plaintiff, defendant, judge) orchestrated by deterministic Lobster workflow. version: 1.0.0 metadata: openclaw: requires: env: - DEEPSEEK_API_KEY - DASHSCOPE_API_KEY bins: - openclaw - lobster primaryEnv: DEEPSEEK_API_KEY emoji: "⚖️" homepage: "https://github.com/baobaodawang-creater/moot-court-ai" --- # Moot Court AI Moot Court AI is an OpenClaw skill that runs a 4-agent Chinese civil court simulation with strict workflow control. ## Agent system - `clerk` (书记员): announces opening, checks identity, controls stage transitions. - `plaintiff` (原告代理律师): argues for plaintiff, presents claim and evidence. - `defendant` (被告代理律师): performs three-validity challenges and defense. - `judge` (审判长): stays neutral, summarizes issues, applies legal syllogism, and renders judgment. ## Model stack - DeepSeek: `deepseek-chat`, `deepseek-reasoner` - Qwen: `qwen-max` (DashScope compatible endpoint) ## Workflow principle - Deterministic orchestration with Lobster. - Agent communication follows fixed hearing stages. - Process follows Chinese civil procedure order (庭前准备 -> 诉辩交换 -> 举证质证 -> 法庭辩论 -> 最后陈述 -> 宣判). ## Installation requirements You must configure both API keys before running: - `DEEPSEEK_API_KEY` - `DASHSCOPE_API_KEY` ## Recommended usage 1. Prepare case files (`case-brief.md`, `complaint.md`, `defense.md`, evidence folders). 2. Initialize materials into agent workspaces. 3. Run `moot-court.lobster` through OpenClaw/Lobster. 4. Export judgment and hearing log for review.