Anomaly Driven Abduction

SkillDev tools

'Tactic: Inductive/abductive path — describe anomalous phenomena, generate

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Anomaly Driven Abduction skill

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/anomaly-driven-abduction/SKILL.md and read by ahel’s review.

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

SOPResponsibilityWhen to call
anomaly-characterizationPrecisely describe the anomalous phenomenon: what was observed, deviation from expectation, conditions of occurrence, excluded trivial explanationsRequired in all modes, execute first
explanation-generationGenerate multiple candidate explanations (abductive hypotheses); each explanation must fully account for the anomalyRequired in all modes, after anomaly-characterization
plausibility-rankingRank 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)

Available SOPs

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

SOPWhen to use
anomaly-characterizationSOP: Describe and classify anomalous phenomena that existing theory cannot explain
explanation-generationSOP: generate a list of candidate explanations for an anomalous phenomenon
plausibility-rankingSOP: rank candidate explanations by plausibility using multi-dimensional weighted scoring

Signals

GitHub stars
469
Forks
37
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
anomaly-driven-abduction
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine