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anomaly-driven-abduction

@yogsoth-ai · 收录于 5 天前 · 上游提交 2 周前

Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility

适合你,如果常需要从异常现象推导可能原因

/ 通过 npx 安装 校验哈希
npx oh-my-skill add yogsoth-ai/de-anthropocentric-research-engine/anomaly-driven-abduction
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- yogsoth-ai/de-anthropocentric-research-engine/anomaly-driven-abduction
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify yogsoth-ai/de-anthropocentric-research-engine/anomaly-driven-abduction
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 59ace64 · 表述以原文为准
它做什么

装上后,Claude 会先要求你精确描述异常现象,然后自动生成至少3个候选解释,最后按合理性排序,并给出排序依据。全程不容许模糊或主观偏好。

什么时候触发

当你描述一个现有理论无法解释的异常现象,并要求进行溯因分析时触发。

装好后可以这样说
Claude 会执行三步分析,输出解释及排序。
Claude 会生成多候选解释,并评估可信度。
技能原文 SKILL.md作者撰写 · Apache-2.0 · 59ace64

Anomaly Driven Abduction

Inductive/abductive path — precisely describe anomalous phenomena that existing theory cannot explain, generate multiple candidate explanations, rank by plausibility, and provide a structured basis for abductive hypotheses.

Orchestration Intent

The starting point of abduction is "surprise" — an observed phenomenon inconsistent with existing theoretical predictions. This tactic forces CC to first precisely describe the anomaly (no vagueness allowed), then systematically generate explanations (not allowed to think of only one), and finally rank by plausibility (no subjective preference allowed).

None of the three steps can be omitted: imprecise description means explanations cannot be focused; insufficient explanations make ranking meaningless; ranking without basis turns hypothesis selection into guesswork.

Available SOPs

| SOP | Responsibility | When to call | |-----|------|---------| | anomaly-characterization | Precisely describe the anomalous phenomenon: what was observed, deviation from expectation, conditions of occurrence, excluded trivial explanations | Required in all modes, execute first | | explanation-generation | Generate multiple candidate explanations (abductive hypotheses); each explanation must fully account for the anomaly | Required in all modes, after anomaly-characterization | | plausibility-ranking | Rank candidate explanations by plausibility criteria (prior probability, explanatory power, parsimony, testability) | Required in all modes, execute last |

Orchestration Pattern

Simplified (S tier, single anomaly)

  • Sequential execution: anomaly-characterization → explanation-generation (≥3 explanations) → plausibility-ranking
  • Applicable: a single clear anomalous phenomenon with sufficient background information

Standard (M tier, 1-3 related anomalies)

  • anomaly-characterization executes independently for each anomaly; explanation-generation generates ≥3 explanations (explanations may be shared across anomalies); plausibility-ranking ranks all explanations uniformly
  • Applicable: multiple related anomalies may have a common explanation, requiring cross-anomaly integration

Deep (L tier, complex anomaly cluster)

  • All 3 SOPs execute; explanation-generation additional requirement: each explanation must state why existing theory cannot explain the anomaly; plausibility-ranking additional output: which explanations can be distinguished by a single experiment
  • Applicable: complex, interrelated anomalous phenomena requiring systematic abductive analysis
Minimum Yield
  • Structured anomaly description: including observed content, deviation from expectation, conditions of occurrence, excluded trivial explanations
  • ≥3 candidate explanations, each explanation:
  • The mechanism that fully explains the anomaly
  • Relationship to existing theory (extend/revise/replace)
  • Ranked list: including each explanation's plausibility score and ranking basis
Yield Report

Report to the calling strategy after execution:

  • Anomaly description completeness (whether it meets HARD-GATE requirements)
  • Number of candidate explanations generated / number ranked
  • Highest-plausibility explanation (for the strategy to prioritize for formalization)
  • Discriminability: which explanations can be distinguished by a single experiment (for reference in subsequent experiment design)

<!-- BEGIN available-tables (generated) -->

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use | | --- | --- | | anomaly-characterization | SOP: Describe and classify anomalous phenomena that existing theory cannot explain | | explanation-generation | SOP: generate a list of candidate explanations for an anomalous phenomenon | | plausibility-ranking | SOP: rank candidate explanations by plausibility using multi-dimensional weighted scoring |

<!-- END available-tables (generated) -->

按 Apache-2.0 许可原样转载,未经改动 · 在 GitHub 查看 →

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