Learn-skills.dev mini-six-ren

小六壬占卜系统 (Mini Six Ren Divination) - 中国传统占卜和命理分析。Use when the user asks for divination, fortune telling, or prediction using mini six ren (小六壬), or mentions keywords: 占卜、算卦、小六壬、三传、运势、占一卦、divination、fortune. Supports three input modes: numbers, date/time, and Chinese characters (汉字笔画). Generates three-pass (三传) predictions with five-element (五行) analysis and LLM-powered interpretation.

install
source · Clone the upstream repo
git clone https://github.com/NeverSight/learn-skills.dev
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/NeverSight/learn-skills.dev "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/skills-md/aicoder2048/mini-six-ren-skill/mini-six-ren" ~/.claude/skills/neversight-learn-skills-dev-mini-six-ren && rm -rf "$T"
manifest: data/skills-md/aicoder2048/mini-six-ren-skill/mini-six-ren/SKILL.md
source content

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

User saysModeExample
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"
"用现在的时间" / "now"
--now
--now
no specific input
--now
default to current time

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

Provider prefixAPI Key env varNotes
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

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