moot-court-ai
Simulate a full Chinese civil court hearing with 4 role-based agents (clerk, plaintiff, defendant, judge) orchestrated by deterministic Lobster workflow.
Best use case
moot-court-ai is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Simulate a full Chinese civil court hearing with 4 role-based agents (clerk, plaintiff, defendant, judge) orchestrated by deterministic Lobster workflow.
Teams using moot-court-ai should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/moot-court-ai/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How moot-court-ai Compares
| Feature / Agent | moot-court-ai | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Simulate a full Chinese civil court hearing with 4 role-based agents (clerk, plaintiff, defendant, judge) orchestrated by deterministic Lobster workflow.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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.
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