anomaly-driven-abduction
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/anomaly-driven-abduction归纳/溯因路径——精确描述无法被现有理论解释的异常现象,生成多个候选解释,按可信度排序,为溯因假设提供结构化基础。
SKILL.md
.github/skills/anomaly-driven-abductionView on GitHub ↗
---
name: anomaly-driven-abduction
description: "Tactic: 归纳/溯因路径——描述异常现象,生成候选解释,按可信度排序"
version: 1.0.0
category: hypothesis-formation
type: tactic
campaign: hypothesis-formulation
sops:
- anomaly-characterization
- explanation-generation
- plausibility-ranking
dependencies:
skills:
- subagent-spawning
---
# Anomaly Driven Abduction
归纳/溯因路径——精确描述无法被现有理论解释的异常现象,生成多个候选解释,按可信度排序,为溯因假设提供结构化基础。
## 编排意图
溯因推理(abduction)的起点是"意外"——观察到的现象与现有理论预测不符。本 tactic 强制 CC 先精确描述异常(不允许模糊),再系统生成解释(不允许只想到一个),最后按可信度排序(不允许主观偏好)。
三步缺一不可:描述不精确则解释无法聚焦;解释不充分则排序无意义;排序无依据则假设选择变成猜测。
## 可用 SOPs
| SOP | 职责 | 何时调用 |
|-----|------|---------|
| anomaly-characterization | 精确描述异常现象:观察到什么、与预期的偏差、发生条件、已排除的平凡解释 | 所有模式必选,首先执行 |
| explanation-generation | 生成多个候选解释(溯因假设),每个解释必须能够完整解释异常 | 所有模式必选,在 anomaly-characterization 之后 |
| plausibility-ranking | 按可信度标准(先验概率、解释力、简洁性、可测试性)对候选解释排序 | 所有模式必选,最后执行 |
## 编排模式
**Simplified(S tier,单一异常)**
- 顺序执行:anomaly-characterization → explanation-generation(≥3 个解释)→ plausibility-ranking
- 适用:单一明确的异常现象,背景信息充分
**Standard(M tier,1-3 个相关异常)**
- anomaly-characterization 对每个异常独立执行;explanation-generation 生成 ≥3 个解释(可跨异常共享解释);plausibility-ranking 对所有解释统一排序
- 适用:多个相关异常可能有共同解释,需要跨异常整合
**Deep(L tier,复杂异常集群)**
- 全部 3 个 SOP 执行;explanation-generation 额外要求:每个解释必须说明为何现有理论无法解释该异常;plausibility-ranking 额外输出:哪些解释可以被单一实验区分
- 适用:异常现象复杂、相互关联,需要系统性溯因分析
## Minimum Yield
- 结构化异常描述:含观察内容、与预期的偏差、发生条件、已排除的平凡解释
- ≥3 个候选解释,每个解释:
- 完整解释异常的机制
- 与现有理论的关系(扩展/修正/替代)
- 排序列表:含每个解释的可信度评分和排序依据
## Yield Report
执行结束后向调用方 strategy 报告:
- 异常描述完整度(是否满足 HARD-GATE 要求)
- 生成候选解释数 / 排序完成数
- 最高可信度解释(供 strategy 优先 formalize)
- 可区分性:哪些解释可以通过单一实验区分(供后续实验设计参考)
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