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/abductive-hypothesis-generation

Strategy: Inference to the best explanation in the face of anomalies

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill abductive-hypothesis-generation --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/abductive-hypothesis-generation

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The summary Claude sees to decide when to auto-load this skill.

Strategy: Inference to the best explanation in the face of anomalies

SKILL.md

abductive-hypothesis-generation.SKILL.md
name: abductive-hypothesis-generation
description: 'Strategy: Inference to the best explanation in the face of anomalies'
version: 1.0.0
category: hypothesis-formation
type: strategy
campaign: hypothesis-formulation
tactics:
- anomaly-driven-abduction
sops:
- anomaly-characterization
- explanation-generation
- plausibility-ranking
- falsifiability-check
dependencies:
  tactics:
  - anomaly-driven-abduction
  sops:
  - falsifiability-check

Abductive Hypothesis Generation

Inference to the best explanation in the face of anomalies: when an anomalous phenomenon that existing theory cannot explain is observed, systematically generate candidate explanations and select the most plausible one as the hypothesis.

When to Use

  • A clear anomalous phenomenon is observed (a result inconsistent with existing theoretical predictions)
  • Existing theory cannot adequately explain a known phenomenon
  • One of several competing explanations must be selected as the most worth testing
  • The research starting point is "this result is strange, why?"

Not applicable: no clear anomaly, just wanting to explore a new field → use inductive-hypothesis-generation instead.

Thinking Framework

**Anomaly → Generate candidate explanations → Rank by plausibility → Best explanation = hypothesis**

The core logic of abductive reasoning:

1. **Anomaly**: precisely describe the anomaly — what phenomenon, inconsistent with what expectation, how large the deviation 2. **Generate candidate explanations**: systematically generate all candidate explanations that can account for the anomaly (no premature filtering) 3. **Rank by plausibility**: rank by plausibility — which explanation is most parsimonious, most consistent with known facts, most testable 4. **Best explanation = hypothesis**: select the most plausible explanation as the working hypothesis, retaining the rest as competing hypotheses

**Core principles of abduction**:

  • **Occam's razor**: when explanatory power is comparable, prefer the explanation with fewer assumptions
  • **Consistency**: the best explanation should not contradict other known facts
  • **Testability**: the best explanation must be able to produce observable predictions (otherwise it cannot be verified)
  • **Generation completeness**: candidate explanations must be exhausted before ranking, to avoid premature convergence

Budget Gate

| Tier | Anomaly description | Candidate explanations | Hypothesis output | Competing hypotheses | |------|---------|---------|---------|---------| | S | 1 precisely described anomaly | ≥2 candidate explanations | 1 best-explanation hypothesis | ≥1 competing hypothesis retained | | M | 1–2 anomalies | ≥3 candidate explanations | ≥2 structured hypotheses | complete plausibility ranking | | L | ≥2 related anomalies | ≥5 candidate explanations | ≥3 structured hypotheses | complete ranking + discriminating prediction design |

Default Reference Flow

1. Call the `anomaly-characterization` SOP: precisely describe the anomaly (phenomenon, expectation, deviation, excluded trivial explanations) 2. Call the `explanation-generation` SOP (via the `anomaly-driven-abduction` tactic): systematically generate candidate explanations (no premature filtering) 3. Call the `plausibility-ranking` SOP: rank candidate explanations by parsimony, consistency, and testability 4. Call the `falsifiability-check` SOP: generate a falsification scenario for the best explanation, confirming its testability

context-checkpoint

Record after each round:

  • Anomaly description (precise version, with deviation quantification)
  • Candidate explanation list (including excluded trivial explanations and exclusion reasons)
  • Plausibility ranking result (including ranking basis)
  • Best-explanation hypothesis + competing hypothesis list
  • Discriminating predictions (what experiment can distinguish the best explanation from competing explanations)

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

Available Tactics

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

| Tactic | When to use | | --- | --- | | anomaly-driven-abduction | Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility |

Available SOPs

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

| SOP | When to use | | --- | --- | | falsifiability-check | SOP: check whether a hypothesis meets the falsifiability criterion |

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

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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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