anomaly-driven-abduction
# ClaudeWave Editor Entry **Anomaly-driven-abduction** structures abductive reasoning for scientific hypothesis formation by enforcing three sequential steps: precise characterization of anomalous phenomena that contradict existing theory, systematic generation of at least three candidate explanations, and plausibility ranking based on prior probability, explanatory power, simplicity, and testability. Use this skill when investigating unexpected observations that current theories cannot adequately explain and need to prioritize candidate hypotheses for further testing or formalization.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/anomaly-driven-abduction && cp -r /tmp/anomaly-driven-abduction/skills/anomaly-driven-abduction ~/.claude/skills/anomaly-driven-abductionSKILL.md
# 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)
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems