Analogy Extraction

SkillDev tools

Extract transferable structural principles from source domains. Orchestrates

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 Analogy Extraction skill

What this skill tells your AI

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

Extract transferable structural principles from source domains.

Stages

Stage 1: Source Identification

Identify candidate source domains using domain-scanning SOP. Evaluate each for structural similarity depth (surface/structural/systemic).

Stage 2: Abstraction

For each promising source, extract the abstract principle using abstraction-extraction or biological-strategy-extraction SOP. Strip domain-specific details to reveal the transferable mechanism.

Stage 3: Structural Mapping

Map source structure to target domain. Identify: corresponding elements, missing elements (gaps), extra elements (opportunities). Use structural-mapping SOP.

Stage 4: Transfer Validation

Assess mapping quality: Is the analogy surface-level (shared labels) or deep (shared relational structure)? Use analogy-quality-assessment SOP. Only deep analogies warrant transfer.

Minimum Yield

MetricFloor
Source domains scanned≥5
Abstractions extracted≥3
Structural mappings completed≥3
Validated deep analogies≥2

Available SOPs

SOPRole
domain-scanningStage 1 — find candidate source domains
web-searchStage 1 — supplement domain search
paper-overviewStage 1 — find academic analogies
abstraction-extractionStage 2 — extract abstract principles
structural-mappingStage 3 — map source→target structure
analogy-quality-assessmentStage 4 — validate mapping depth
novelty-scoringPost — score resulting ideas
idea-synthesisPost — synthesize into coherent concepts

Signals

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