Moot Court AI

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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"

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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.