blend-construction
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/blend-constructionConstruct complete 4-space blends with emergent structure following the Fauconnier-Turner conceptual integration network model.
SKILL.md
.github/skills/blend-constructionView on GitHub ↗
--- name: blend-construction description: Construct complete 4-space blends with emergent structure. Orchestrates input-space-construction → generic-space-extraction → blend-composition. execution: tactic used-by: combinatorial-creativity, concept-blending, multi-level-bisociation, emergent-property-hunting --- # Blend Construction Construct complete 4-space blends with emergent structure following the Fauconnier-Turner conceptual integration network model. ## Stages ### Stage 1: Input Space Construction Build rich input spaces for both source concepts using input-space-construction SOP. Each space must include elements, relations, attributes, and internal logic. ### Stage 2: Generic Space Extraction Extract the shared abstract structure from both input spaces using generic-space-extraction SOP. The generic space captures what the two inputs have in common at the most abstract level. ### Stage 3: Blend Composition Compose the blended space by selectively projecting structure from both inputs and creating new connections using blend-composition SOP. The blend must develop emergent structure not present in either input. ## Minimum Yield | Metric | Floor | |--------|-------| | Complete 4-space blends | ≥2 | | Emergent structures per blend | ≥1 | | Vital relations compressed | ≥3 per blend | | Novel connections in blend | ≥2 per blend | ## Available SOPs | SOP | Role | |-----|------| | input-space-construction | Stage 1 — build input spaces | | generic-space-extraction | Stage 2 — extract shared structure | | blend-composition | Stage 3 — compose blended space | | blend-completion | Post-Stage 3 — recruit background knowledge | | vital-relation-mapping | Pre-Stage 1 — map vital relations to guide projection |
More from yogsoth-ai/de-anthropocentric-research-engine
- abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
- ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
- ablation-component-mappingMap system architecture to ablatable units for ablation studies
- ablation-designDesign ablation studies to isolate component contributions in ML systems
- ablation-executionRemove components one by one from a system, record the response/impact of each removal.
- abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
- abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
- abstraction-ladderPerform bisociation at multiple abstraction levels
- abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
- abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.