bazi-compatibility-reading
把一份已完成的八字合婚 JSON 文件、或等价的经过核对的比较结果,解读成一份平衡、分方向、逐条附依据的关系报告。适用于合婚计算器交来结果之后,或有人要求解释已经算出的总体、恋爱、婚姻、友情、家庭、工作分数时。不适用于原始出生记录、单人解读、重新计算、预测,或要一个非黑即白的缘分结论。
- 分组
- 命理
- 版本
- 0.8.4
- 许可证
- MIT
安装
/plugin install chinese-metaphysics@misoto22然后这样调用:
/chinese-metaphysics:bazi-compatibility-reading这个技能读已经算好的合婚数据,写一份 1800 到 2400 字的关系报告。它有两个刻意的约束。一是「分方向」:A 对 B 的影响和 B 对 A 的影响是两件事,报告分别陈述,而不是给出一个混在一起的双向结论。二是「平衡」:有利与不利的相互作用都要写,不能只挑好听的说。它拒绝输出「合不合适」这种二元判断——数据能说明哪些方面会顺、哪些方面需要经营,但不能替两个人决定要不要在一起。
什么时候会触发
CI 实际用来评测这个技能的 prompt——所以它们不会和技能的真实行为脱节。
会触发
- Interpret the verified bazi_compatibility_A_B.json that was just generated.
- 解读这个已经算好的八字合盘 JSON,重点说五维分数和不对称支持。
- Below are both identities, five ledgers, general arithmetic, context profile, confidence, and sensitivity variants. Explain the result.
不会触发
- A 是 1990-03-14 07:42 上海,B 是 1992-06-08 14:20 深圳,合一下八字。
- Read my existing BaZi chart and explain my day master.
- Use this comparison to predict when we will break up.
BaZi Compatibility Reading
Read one complete compatibility calculation and write bazi_compatibility_reading_<name-a>_<name-b>.md plus bazi_compatibility_evidence_<name-a>_<name-b>.md. Interpret its evidence; never recalculate either chart or any score. The first is a reader report; the second is a separate evidence artifact.
Before writing either file, read and follow shared/report-presentation.md. It defines the common localization, data-card, and separate-evidence boundary rules; this skill defines the compatibility evidence, direction, and section requirements.
Route and validate
- Two raw birth records, two chart files, or one of each belong to
bazi-compatibilityfirst. - One person's chart belongs to
bazi-reading. - Forecasts, auspicious dates, and event timing are outside this release.
Accept a chinese-metaphysics.bazi-compatibility JSON artifact at schema version 1 with a valid canonical checksum, or complete pasted equivalent data. Pasted data must include two names and chart checksums, model version, all five dimensions with weights/scores/ledgers, general score, optional contextual profile and score, confidence, and every sensitivity variant. Mark complete pasted input pasted-complete; do not claim checksum validation when none exists.
Use the vendored shared/bazi/artifacts.py validator for JSON. Stop on checksum mismatch, unsupported version, missing identity, wrong weights, incomplete positive or negative ledger, arithmetic mismatch, a contextual score without its profile, or an alternate range without variants. Name the defect and route source records to bazi-compatibility; never reconstruct a score.
Evidence index before prose
Preserve the calculator's exact evidence and ownership before writing prose:
[D-element],[D-support],[D-interactions],[D-day-core],[D-stability]for dimensions.- Preserve every dimension ledger id such as
[support.received.left]and[cross.branch_clash.day.month]. [G-score]for the 25/20/20/20/15 general arithmetic.[C-score]for an explicitly selected contextual profile.[S-primary-alternate]style ids for sensitivity variants.
Use this index to build the evidence artifact's heading-based claim map. Compatibility mappings must also record the stored owner or direction. Do not expose raw ids or stored ownership labels in the reader report.
Reader-report directional claims must name both people in the form Name A → Name B; describe what A supplies or what B receives in plain language. Reserve stored left and right ownership labels for the evidence artifact. Shen Sha may appear only when present in complete source evidence, with [SS-...] and secondary labels in the evidence artifact; it never explains a numeric dimension.
Reading discipline
- Scores are versioned heuristics, not probabilities, success rates, scientific measurements, or predictions.
- When a relationship context is explicitly selected, show its contextual score first in the reader layer and label the general score as a secondary reference. When
relationship_typeis null, show the general score as the displayed result. A contextual score answers only the explicitly named romance, marriage, friendship, family, or work lens. - Do not invent context from names, genders, or a vague request. If
relationship_typeis null, omit the selected-context analysis rather than guessing. - Preserve multidimensional disagreement. High affinity with low stability, or high complementarity with asymmetric support, must remain two findings rather than collapse into “compatible” or “incompatible.”
- Treat stem/branch combinations as ease or linkage hypotheses and clashes, harms, or breaks as friction hypotheses. Neither is automatically good or bad outside its ledger context.
- Describe asymmetry directly: what A supplies B can differ from what B supplies A. Do not turn unequal support into moral blame or a hierarchy.
- Use conditional language: “in this traditional model,” “may be experienced as,” and “suggests a discussion point.”
- Do not say soulmate, destined, karmically bound, toxic, safe/unsafe, marry/divorce, hire/fire, or predict duration, fidelity, abuse, fertility, money, health, or a future event.
- Give low-risk communication and boundary prompts, not medical, legal, financial, employment, or relationship commands.
Sensitivity
The primary-primary calculation supplies the displayed score: show its contextual score first when a context is selected, or its general score when relationship_type is null. When alternates exist, explain the minimum, maximum, spread, and which source boundary changes. Do not average variants, select the best result, or describe the range as odds. Lower claim confidence when a major finding changes across variants.
Required report order
Keep exactly these six reader-report sections, using the structure in references/output-template.md:
- Relationship pattern
- What draws the pair together
- Main misalignment
- Each person's likely experience
- What to observe now
- Model data card
Lead with a conditional relationship pattern, but do not lead with score, source confidence, or a method disclaimer. Develop the attraction, friction, and two directional experiences to the depth required by shared/report-presentation.md; do not reduce the reader report to a score summary. Use sections 2–5 to preserve mixed dimensions, explain named directional support, and offer low-risk observations. When a relationship context is explicitly selected, show its contextual score first in section 6 and label the general score as a secondary reference; otherwise show the general score and state that no context was selected. Do not invent a context.
In section 4, describe each person's likely experience separately using Name A → Name B direction labels, never stored left or right labels. Section 5 gives no more than three observable, reversible prompts. Keep the data card small: the displayed score and no more than three supporting indicators.
Write the separate evidence artifact after the reader report. In addition to the shared evidence requirements, include source status, both source-chart checksums, the comparison checksum when available, exact five-dimensional weights and values, general and contextual weighted arithmetic, confidence, sensitivity variants, and stored owner or direction for compatibility evidence.
Write safely
Write UTF-8 Markdown only after validation. Use portable source names in bazi_compatibility_reading_<name-a>_<name-b>.md and bazi_compatibility_evidence_<name-a>_<name-b>.md; preserve display names inside both files. Reuse an existing report pair only when both record the same compatibility checksum; otherwise append the first eight checksum characters to both names. Do not overwrite another comparison, alter the source JSON, invoke chart calculation, or create new numeric scores.
Report the reader-report path, evidence-artifact path, and source compatibility JSON path. See references/examples.md for asymmetric support, mixed dimensions, sensitivity, and corrupt-source handling.