analogical-transfer
The analogical-transfer skill applies Gentner's structure-mapping theory to systematically identify relational correspondences between domains and transfer higher-order constraints from source to target problems. Use this when solving novel problems by discovering distant domains sharing similar abstract relational structures, extracting their deep structural patterns, and adapting those principles to new contexts while filtering for substantive rather than superficial analogies.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/analogical-transfer && cp -r /tmp/analogical-transfer/skills/analogical-transfer ~/.claude/skills/analogical-transferSKILL.md
# Analogical Transfer Systematic structure-mapping from source to target domain following Gentner's structure-mapping theory. Prioritize relational similarity over surface similarity. ## State Ledger | Resource | Target | Current | % | |----------|--------|---------|---| | web-search | 25 | 0 | 0% | | web-research | 10 | 0 | 0% | | paper-overview | 30 | 0 | 0% | | paper-search | 20 | 0 | 0% | | paper-research | 8 | 0 | 0% | ## 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 | Tactic | Role | |--------|------| | analogy-extraction | Core tactic — extract and validate structural analogies | | domain-divergence | Find distant source domains with high structural similarity | | bridge-validation | Validate mapping depth before transfer | ## Available SOPs | SOP | Role | |-----|------| | domain-scanning | Find candidate source domains | | abstraction-extraction | Extract abstract relational structure | | structural-mapping | Map source→target correspondences | | analogy-quality-assessment | Rate analogy depth (surface/structural/systemic) | | transfer-adaptation | Adapt transferred principle to target constraints | | cross-domain-synthesis | Synthesize 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 <!-- BEGIN available-tables (generated) --> ## Available Tactics Optional, no fixed order; the final leaf is always a sop. | Tactic | When to use | | --- | --- | | bridge-validation | Validate analogy depth and transfer viability. Ensures only deep structural analogies (not surface-level similarities) proceed to transfer. | | domain-divergence | Scan 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. | SOP | When to use | | --- | --- | | abstraction-extraction | Extract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms. | | analogy-quality-assessment | Assess analogy depth (surface/structural/systemic). Determines whether an analogy warrants transfer investment. | | cross-domain-synthesis | Synthesize all cross-domain findings into a structured idea report. Integrates outputs from all strategies and SOPs. | | structural-mapping | Map source→target structural correspondences. Identifies corresponding, missing, and extra elements between domains. | | transfer-adaptation | Adapt transferred principle to target problem constraints. Produces concrete adapted solutions from abstract principles. | <!-- END available-tables (generated) -->
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems