formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced…
Strategy: Inference to the best explanation in the face of anomalies
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill abductive-hypothesis-generation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/abductive-hypothesis-generationContext preview
The summary Claude sees to decide when to auto-load this skill.
Strategy: Inference to the best explanation in the face of anomalies
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
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.
Not applicable: no clear anomaly, just wanting to explore a new field → use inductive-hypothesis-generation instead.
**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**:
| 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 |
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
Record after each round:
<!-- BEGIN available-tables (generated) -->
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 |
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) -->
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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