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mini-six-ren

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Updated 10-15 16:43

About this skill

Mini Six Ren Divination (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

User says Mode Example
gives 3 numbers --numbers --numbers 3,5,7
mentions a date/time --datetime --datetime "2025-01-31 14:30"
gives Chinese characters --chars --chars "天地人"
"use the current time" / "now" --now --now
no specific input --now default to current time

JSON Schema (key analytical fields)

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

Field Purpose
input Input mode metadata (mode, raw, lunar/hour for datetime modes, stroke_counts for chars)
input_numbers The three step numbers used for 三传 computation
passes The three passes; each has position, role (体/枢/用), symbol with auspice/auspice_level/question_lens
relations Pairwise five-element relations between adjacent passes, with meaning
subject_object 体用关系: subject (initial) vs object (final) — relation + interpretation
flow Overall five-element flow pattern (连珠相生 / 连珠相克 / 三同比和 / 始克终生 / ... ) with explanation
patterns overall_pattern (三吉/二吉一凶/...), grade (上上格..下下格), trend (渐入佳境/苦尽甘来/...), auspice_score
combinations Detected special combinations (双符 / 三符格局) with valence
timing_guidance Season, months, hours, directions (based on末传 element + 三传 auspice-sorted directions), favorable deities
question Echo of the user's question

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: Identify Question Category (识别问题类别)

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

Category Keywords
Wealth money, wealth, business, investment, raise, bonus, buying and selling
Romance dating, feelings, marriage, partner, reconciliation, breakup
Career work, project, promotion, job change, offer, interview
Health illness, surgery, recovery, examination, physical checkup
Studies exam, further education, studying abroad, thesis, study
Family family members, parents, children, spouse, family matters
Legal disputes lawsuit, litigation, dispute, law
Travel business trip, travel, moving house, long journey, transfer

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 (cause of the problem / the questioner themselves)
  • 末传 = 用 (the matter asked about / the final outcome)
  • 中传 = 枢 (process pivot)

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 → Romance +2 great fortune, Career -1 minor misfortune
  • 大安 → Health +2 great fortune, Travel -1 minor misfortune (favors stillness over activity)
  • 速喜 → Wealth +2 great fortune, Health -1 minor misfortune (sudden illness)

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

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

  • 连珠相生 → fortune flows continuously, the most auspicious flow
  • 连珠相克 → each level is constrained, the most inauspicious flow
  • 始克终生 → bitterness first, sweetness later
  • 始生终克 → prosperity at its peak gives way to decline
  • 三同比和 → power is concentrated, hard to change suddenly
  • etc.

Step 5: Special Combination Detection (Detect Combinations)

Use combinations list:

  • Three-token pattern → defines the entire cast.
  • Two-token combination: valence "+" reinforces auspicious signs / "-" warns of danger / "0" depends on actions
  • If empty: skip; rely on basic analysis.

See references/combinations_reference.md for detailed combination meanings.

Step 6: Timing & Direction (Timing & Direction)

Use timing_guidance:

  • Timing: season, months, hours (based on the element of the final transmission)
  • Auspicious direction: favorable_directions_from_passes
  • Directions to avoid: avoid_directions_from_passes
  • Spiritual beings that can be invoked: favorable_deities

Step 7: Synthesis & Advice (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. 🎯 Hexagram Overview (ai_overview) — one paragraph: overall pattern + five-element flow + trend (1-2 sentences to set the tone)
  2. ⏳ Timeline (ai_timeline) — three sections: initial transmission / middle transmission / final transmission, each 2-4 sentences with a question lens
  3. ⚖️ Body/Function and Five Elements (ai_subject_object_flow) — explain "why the situation is heading this way": body-function relationship × five-element flow
  4. 🌟 Key Combinations (ai_combinations) — 1-2 most relevant combinations, or note that none formed
  5. 🧭 Timing and Direction (ai_timing_direction) — when / where / which deity to invoke
  6. 💡 Action Advice (ai_advice) — 3-5 concrete actionable bullets
  7. 🔮 Conclusion (ai_conclusion) — 1-2 sentences: direct answer to the question

Style Guidelines

  • Always stay tightly focused on the user's question—never speak in generalities.
  • The final transmission is the most important (final pass = primary indicator).
  • Avoid extreme assertions—use "宜" "忌" "可" "需" etc. to leave room.
  • Elegant, philosophical Chinese, but accessible.
  • Total length: 700-1100 Chinese characters 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

Provider prefix API Key env var Notes
openai OPENAI_API_KEY GPT series
anthropic ANTHROPIC_API_KEY Claude series
google-gla GEMINI_API_KEY Gemini series
deepseek DEEPSEEK_API_KEY DeepSeek
kimi MOONSHOT_API_KEY Moonshot Kimi
qwen DASHSCOPE_API_KEY Alibaba Qwen
glm ZHIPU_API_KEY Zhipu ChatGLM

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