Mini Six Ren (小六壬占卜)

Mini Six Ren (小六壬占卜)

Chinese traditional divination system based on the Nine-Palace hand technique. Generate three-pass (三传) predictions and provide AI-powered analysis.

Workflow

1. Determine input mode (numbers / datetime / Chinese chars / current time)
2. Run scripts/​xiaoliu.py --format json to compute the prediction
3. Check if config.yaml exists and has a model field:
   • No config (default): Display ℹ️ 当前使用 Claude Code 内置模型解读。如需使用第三方模型，请创建 config.yaml。Then use the built-in LLM to analyze the result following the "LLM Analysis" section below.
   • Has config + API key: Display ℹ️ 当前使用 <model> 解读。Run scripts/​interpret.py with the prediction JSON piped in:

     uv run scripts/​xiaoliu.py --now --question "问题" --format json | \
       uv run scripts/​interpret.py --question "问题"
     

   • Has config, missing API key: Display ⚠️ 请在 .env 中设置 <ENV_KEY>。Fall back to built-in LLM analysis.
4. Format the report using assets/​template.md

Quick Start

Run the divination script (uv single-file script, no project install needed):

# By three numbers
uv run scripts/​xiaoliu.py --numbers 1,2,3 --question "今日运势" --format json

# By date/​time (converts to lunar calendar internally)
uv run scripts/​xiaoliu.py --datetime "2025-07-15 10:30" --question "面试能成功吗" --format json

# By Chinese characters (uses stroke count)
uv run scripts/​xiaoliu.py --chars "天地人" --question "感情运势" --format json

# By current time
uv run scripts/​xiaoliu.py --now --question "今天适合出行吗" --format json

Use --format json to get structured output for LLM analysis. Use --format text for human-readable display.

Input Mode Selection

LLM Analysis

After getting the JSON prediction result, provide an analysis following this structure. Role-play as a 小六壬占卜大师 with deep traditional culture knowledge.

Analysis structure

1. 卦象总览: Summarize the three passes and their elements
2. 时间发展脉络:
   • 初传（前期/当前）: What the first symbol means for this question
   • 中传（中期/发展）: How the middle symbol drives change
   • 末传（后期/结果）: What the final symbol predicts
3. 五行生克解读: Explain how the element relationships affect the outcome
4. 具体建议: Practical, actionable advice tied to the question
5. 关键提示: Notable directions, timing, or deity influences

Analysis guidelines

• Always tie the interpretation to the specific question asked
• Prioritize the final pass (末传) as the most important indicator
• Explain five-element relationships in terms the user understands
• Keep analysis under 800 characters (Chinese) for conciseness
• Use elegant, philosophical Chinese language but remain accessible

Report Output

After generating the prediction and LLM analysis, format using assets/​template.md. Replace all {{placeholder}} variables with actual values from the script output and LLM analysis.

Third-Party Model Configuration (Optional)

By default, the skill uses Claude Code's built-in LLM for interpretation. To use a third-party model instead:

Step 1: Create config.yaml

Create config.yaml in the skill root directory (mini-six-ren/​config.yaml):

# 格式: provider:model_name
model: deepseek:deepseek-chat

Format: provider:model_name

Step 2: Set API Key in .env

Add your API key to mini-six-ren/​.env:

DEEPSEEK_API_KEY=sk-...

Supported Providers

Examples

# DeepSeek
model: deepseek:deepseek-chat

# GPT-4o
# model: openai:gpt-4o

# Qwen
# model: qwen:qwen-plus

To switch back to built-in LLM, simply delete config.yaml.

Reference

For detailed symbol meanings and five-element relationships: see references/​symbols_reference.md

For usage examples: see the examples/ directory