domain-divergence
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/domain-divergenceScan and select maximally diverse source domains to maximize creative potential.
SKILL.md
.github/skills/domain-divergenceView on GitHub ↗
--- name: domain-divergence description: Scan and select maximally diverse source domains. Ensures creative search covers genuinely unrelated fields with high transfer potential. execution: tactic used-by: cross-domain-discovery, facet-bisociation, analogical-transfer, random-stimulus-entry, forced-bridge-construction, design-by-analogy --- # Domain Divergence Scan and select maximally diverse source domains to maximize creative potential. ## Stages ### Stage 1: Domain Scanning Use domain-scanning SOP to identify candidate source domains. Cast a wide net across sciences, arts, engineering, biology, social systems, mathematics, and everyday life. ### Stage 2: Random Paper Entry Inject randomness via random-paper-entry SOP. Select papers from unexpected fields to break domain fixation and discover overlooked source domains. ### Stage 3: Random Word Stimulus Use random-word-stimulus SOP to generate additional domain candidates that pure search would miss. Random words point to concrete domains (e.g., "coral" → marine biology → reef self-organization). ### Stage 4: Evaluate Diversity Assess the collected domains for genuine diversity: - No two domains should share the same parent discipline - Domains should span at least 3 of: natural science, social science, engineering, arts, mathematics, everyday life - Each domain must have identifiable transfer potential to the target problem ## Minimum Yield | Metric | Floor | |--------|-------| | Domains scanned | ≥8 | | Unrelated domains identified | ≥3 | | Domains with transfer potential | ≥3 | | Discipline categories covered | ≥3 | ## Available SOPs | SOP | Role | |-----|------| | domain-scanning | Stage 1 — systematic domain search | | random-paper-entry | Stage 2 — random paper as domain pointer | | random-word-stimulus | Stage 3 — random word as domain pointer | | abstraction-extraction | Stage 4 — verify transfer potential via abstraction | | analogy-quality-assessment | Stage 4 — assess structural similarity depth |
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.