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Zengshan Buei Six-Line Divination Agent icon

Zengshan Buei Six-Line Divination Agent

Knowledge Management Updated 2026.08.30

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About this skill

Problem It Solves

Traditional Six-Line divination relies on many mnemonics and domain heuristics. If a model answers freely, it can easily mix schools, skip steps, or fabricate hexagram data. liuyao turns the Zengshan Buei framework into an inspectable workflow: define the question, select the useful spirit, judge strength, map the four generative/controlling forces, check special patterns, determine timing, and produce a structured report. It also stores each reading as case JSON for calibration and review.

How It Works

  • Layered questioning: asks 1-2 context-dependent questions by category, such as investment versus salary, or acute illness versus chronic illness.
  • Casting and assembly: after receiving six numbers, calls scripts/zhuanghua.py to generate the hexagram instead of calculating it ad hoc.
  • Calibration validation: uses the first four steps to infer past conditions and asks the user to confirm options, with up to three retry angles.
  • Rule retrieval: consults matching files under knowledge/rules/ for useful spirit, strength, chong/he, hidden spirit, and timing; falls back to scripts/faiss_search.py when needed.
  • Case memory: writes casting, calibration, analysis, and feedback into case JSON files to support Cold/Warm/Hot growth and reviews.

Boundaries and Caveats

The skill is limited to Six-Line divination and knowledge management; it should not be treated as medical diagnosis or legal advice. When the data is unclear, it should say so rather than force a reading. Outputs should preserve confidence and avoid promising certainty.

Use Cases

  • After a querent describes an issue, ask targeted follow-ups, cast the hexagram via script, and produce a structured reading report.
  • Given six casting numbers, call the assembly script and check yongshen against month, day, moving lines, and transformed lines.
  • Persist casting, calibration, analysis, and feedback into case JSON files for later review and rule updates.
  • When rule files do not cover a case, retrieve relevant experience with FAISS and present it with confidence notes.

Best For

  • Yi-knowledge maintainers who need to encode mnemonics into retrievable rules and case review workflows.
  • Vertical-agent engineers who want to break a domain workflow into asking, casting, calibration, and reporting steps.
  • LLM rule-following researchers who want to inspect how models stay inside a fixed divination system.
  • Consultation record keepers who need standardized case JSON fields and post-feedback review triggers.