Mini Six Ren

Mini Six Ren (小六壬占卜)

Chinese traditional divination based on the Nine-Palace hand technique (九宫掌诀). Generates three-pass (三传) predictions with multi-layer analysis, then provides AI-powered interpretation grounded in traditional methodology.

Workflow

1. Determine input mode (numbers / datetime / Chinese chars / current time).
2. Run scripts/xiaoliu.py --format json to compute the prediction (now returns a rich analytical JSON — see "JSON Schema" below).
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 perform the built-in LLM analysis following the "Seven-Step Interpretation Framework" below.
   • Has config + API key: Display ℹ️ 当前使用 <model> 解读. Pipe the prediction JSON into scripts/interpret.py:

     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 final report using assets/template.md, filling all placeholders with the prediction JSON and the LLM analysis.

Quick Start

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

# By date/time (auto-converted to lunar month / day / hour-branch)
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 for LLM analysis. Use --format text for direct human-readable display (already includes the analytical sections).

Input Mode Selection

JSON Schema (key analytical fields)

The script now outputs a structured JSON with these top-level fields:

Key field for question-aware interpretation: passes[i].symbol.question_lens provides each symbol's auspice level (-2..+2) for each of 8 question categories (财运/情感/事业/健康/学业/家庭/官非/出行). Always prefer the lens value over the base auspice_level when the user's question category is identifiable.

Seven-Step Interpretation Framework

When performing the built-in LLM analysis (Claude Code's own model, no third-party config), follow this rigorous seven-step traditional framework. (scripts/interpret.py embeds the same framework in its system prompt for third-party models.)

Step 1: 识别问题类别 (Identify Question Category)

Map the user's question to one of 8 categories — this determines which question_lens index to use:

If no clear question or "综合运势": fall back to base auspice_level.

Step 2: 体用定位 (Subject-Object Positioning)

• 初传 = 体 (problem原因 / 问者本身)
• 末传 = 用 (所问事 / 最终结果)
• 中传 = 枢 (过程枢纽)

Use subject_object.interpretation from the JSON.

Step 3: 三传时序分析 (Sequential Analysis with Question Lens)

For each pass, answer:

1. In this question category, what does this symbol mean? — Use question_lens[category] from each pass's symbol.
2. Five-element relation with the adjacent pass — Use relations[i].meaning.
3. Direction and deity — what energy does this position channel?

Critical: the same symbol means different things across categories. Examples:

• 桃花 → 情感 +2 大吉, 事业 -1 小凶
• 大安 → 健康 +2 大吉, 出行 -1 小凶 (主静不主动)
• 速喜 → 财运 +2 大吉, 健康 -1 小凶 (突发病症)

Step 4: 五行整体流转 (Overall Five-Element Flow)

Use flow.pattern + flow.explanation + patterns.trend:

• 连珠相生 → 气运绵延，最吉流转
• 连珠相克 → 层层受制，最凶流转
• 始克终生 → 先苦后甜
• 始生终克 → 盛极而衰
• 三同比和 → 力量集中难突变
• 等等

Step 5: 特殊组合识别 (Detect Combinations)

Use combinations list:

• 三符格局 → defines the entire cast.
• 双符组合: valence "+" 强化吉象 / "-" 警示凶险 / "0" 取决于行动
• If empty: skip; rely on basic analysis.

See references/combinations_reference.md for detailed combination meanings.

Step 6: 应期与方位 (Timing & Direction)

Use timing_guidance:

• 应期: season, months, hours (based on末传 element)
• 吉方: favorable_directions_from_passes
• 避方: avoid_directions_from_passes
• 可借神灵: favorable_deities

Step 7: 综合判断与建议 (Synthesis & Advice)

Tie everything back to the user's specific question. Give a clear answer (能成 / 不能成 / 部分成 / 需某条件).

Built-in LLM Output Structure

Generate analysis in this exact section order (matches assets/template.md placeholders):

1. 🎯 卦象总览 (ai_overview) — one paragraph: 整体格局 + 五行流转 + 趋势 (1-2 sentences定调)
2. ⏳ 时间脉络 (ai_timeline) — 三段：初传 / 中传 / 末传, each 2-4 sentences with question lens
3. ⚖️ 体用与五行 (ai_subject_object_flow) — explain "why事态如此走向": 体用关系 × 五行流转
4. 🌟 关键组合 (ai_combinations) — 1-2 most relevant combinations, or note that none formed
5. 🧭 应期与方位 (ai_timing_direction) — when / where / which deity to invoke
6. 💡 行动建议 (ai_advice) — 3-5 concrete actionable bullets
7. 🔮 结论 (ai_conclusion) — 1-2 sentences: direct answer to the question

Style Guidelines

• 始终扣紧用户问题——never泛泛而谈。
• 末传最重要 (final pass = primary indicator).
• 避免极端断言——用"宜""忌""可""需"等留余地。
• Elegant, philosophical Chinese, but accessible.
• Total length: 700-1100 中文字 for the LLM analysis portion.

Report Output

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

Key template variables

From xiaoliu.py JSON output:

• {{timestamp}}, {{input_mode}}, {{input_data}}, {{input_numbers}}, {{question}}
• {{overall_pattern}}, {{grade}}, {{auspice_score}}, {{trend}}, {{trend_explanation_short}}
• {{flow_pattern}}, {{element_sequence}}, {{flow_explanation}}
• Per pass: {{first_name}}, {{first_element}}, {{first_auspice}}, {{first_level}}, {{first_description}}, {{first_interpretation}}, {{first_direction}}, {{first_deity}}, {{first_lens_value}} (and second_*, third_* analogously)
• {{relation_1_2}}, {{relation_2_3}}, {{relation_1_2_meaning}}, {{relation_2_3_meaning}}
• {{subject_object_relation}}, {{subject_object_interpretation}}, {{subject_object_summary}}, {{subject_object_relation_short}}
• {{primary_element}}, {{element_spirit}}, {{season}}, {{favorable_months}}, {{favorable_hours}}, {{element_directions}}, {{favorable_directions_from_passes}}, {{avoid_directions_from_passes}}, {{favorable_deities}}, {{favorable_colors}}
• {{#combinations}}...{{/combinations}} block: iterates over the combinations array, each item has {{combo_name}}, {{combo_type}}, {{combo_symbols}}, {{combo_meaning}}. If empty, replace the whole loop with the note "本卦三传无形成传统特殊组合，回归基础卦象分析。"

From LLM analysis (you generate these strings):

• {{ai_overview}}, {{ai_timeline}}, {{ai_subject_object_flow}}, {{ai_combinations}}, {{ai_timing_direction}}, {{ai_advice}}, {{ai_conclusion}}
• For question-lens explanations: {{first_lens_explanation}}, {{second_lens_explanation}}, {{third_lens_explanation}} (brief 1-line explanation tying the lens value to the user's question)

Third-Party Model Configuration (Optional)

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

Step 1: Create config.yaml

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

Step 2: Set API Key in .env

DEEPSEEK_API_KEY=sk-...

Supported Providers

interpret.py embeds the same seven-step framework as a system prompt, so third-party models produce comparably structured output. To switch back to the built-in LLM, delete config.yaml.

References

• references/symbols_reference.md — 九宫符号详解 (按 8 类问题分化)
• references/interpretation_framework.md — 七步解读框架方法论
• references/combinations_reference.md — 双符 / 三符特殊组合谱
• examples/ — 三种输入方式示例