- 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
- bins
- 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_KEYDASHSCOPE_API_KEY
Recommended usage
- Prepare case files (
case-brief.md,complaint.md,defense.md, evidence folders). - Initialize materials into agent workspaces.
- Run
moot-court.lobsterthrough OpenClaw/Lobster. - Export judgment and hearing log for review.