Analogical Transfer

SkillDev tools

Systematic structure-mapping from source to target domain (Gentner).

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 Analogical Transfer skill

What this skill tells your AI

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

Systematic structure-mapping from source to target domain following Gentner's structure-mapping theory. Prioritize relational similarity over surface similarity.

State Ledger

ResourceTargetCurrent%
web-search2500%
web-research1000%
paper-overview3000%
paper-search2000%
paper-research800%

HARD-GATE

Cannot exit strategy until ≥80% of each budget line is consumed OR yield targets are met with justification for remaining budget.

Available Tactics

TacticRole
analogy-extractionCore tactic — extract and validate structural analogies
domain-divergenceFind distant source domains with high structural similarity
bridge-validationValidate mapping depth before transfer

Available SOPs

SOPRole
domain-scanningFind candidate source domains
abstraction-extractionExtract abstract relational structure
structural-mappingMap source→target correspondences
analogy-quality-assessmentRate analogy depth (surface/structural/systemic)
transfer-adaptationAdapt transferred principle to target constraints
cross-domain-synthesisSynthesize transfer outputs

Execution Guidance

  1. Functionalize target: Restate target problem in relational terms (not object terms)
  2. Source search: Use domain-scanning to find domains with similar relational structure
  3. Abstract source: Extract relational structure from source using abstraction-extraction
  4. Map structure: Use structural-mapping to align source→target correspondences
  5. Assess depth: Apply analogy-quality-assessment — only proceed with STRUCTURAL or SYSTEMIC matches
  6. Transfer: Carry over higher-order relational constraints from source to target
  7. Adapt: Use transfer-adaptation to fit transferred principles to target constraints
  8. Validate: Confirm transferred solution respects target domain physics/logic

Available Tactics

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

TacticWhen to use
bridge-validationValidate analogy depth and transfer viability. Ensures only deep structural analogies (not surface-level similarities) proceed to transfer.
domain-divergenceScan and select maximally diverse source domains. Ensures creative search covers genuinely unrelated fields with high transfer potential.

Available SOPs

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

SOPWhen to use
abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
analogy-quality-assessmentAssess analogy depth (surface/structural/systemic). Determines whether an analogy warrants transfer investment.
cross-domain-synthesisSynthesize all cross-domain findings into a structured idea report. Integrates outputs from all strategies and SOPs.
structural-mappingMap source→target structural correspondences. Identifies corresponding, missing, and extra elements between domains.
transfer-adaptationAdapt transferred principle to target problem constraints. Produces concrete adapted solutions from abstract principles.

Signals

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