cross-domain-synthesis

name	cross-domain-synthesis
description	Imports structural mechanisms from distant domains to generate novel ideas. Use when dream seeds are coherent but unsurprising — good quality but lacking novelty. Activates on well-formed but predictable seed patterns.
compatibility	google-adk
metadata	{"scope":"dreamer","triggers":["coherent but unsurprising seeds","predictable idea patterns","seeds that feel \"correct but boring\""],"requires_tools":["append_to_state"]}

Cross-Domain Synthesis Method

You are activating the Cross-Domain Synthesis creative method. This skill breaks predictability by importing structural mechanisms from distant knowledge domains.

When to Use

• Seeds are well-formed and coherent but lack surprise
• Ideas feel like obvious next steps rather than creative leaps
• The dream space needs injection of genuinely new structural patterns

Key Principle

Import mechanisms, not metaphors.

Bad synthesis: "Memory is like a river" (metaphor — decorative, not structural) Good synthesis: "Memory uses the mechanism rivers use — erosion creates channels that make future flow more likely in the same direction, but floods can carve entirely new paths"

4-Step Structural Import Method

Step 1: Abstract the Shape

Read the current dream_seeds from state. For each seed, identify its abstract structural shape — the pattern stripped of domain-specific details.

Example:

• Seed: "Build a priority queue for processing dream seeds"
• Shape: "Items compete for limited processing attention based on assigned scores"
• Abstract: "Competitive allocation under scarcity"

Common shapes:

• Competitive allocation (priority queues, markets, evolution)
• Feedback amplification (compound interest, epidemics, rumors)
• Phase transition (water→ice, startup→scale, individual→movement)
• Symbiotic coupling (gut bacteria, pollination, trade)
• Signal/noise separation (immune system, spam filter, attention)

See references/domain-shape-library.md for 20 shapes with donor domains.

Step 2: Find Donor Domains

For the abstract shape, identify 2-3 distant donor domains where this shape appears in a dramatically different context. Distance is key — the further the domain from AI/software, the more surprising the synthesis.

Distance ranking (prefer bottom):

1. Other software patterns (closest — avoid)
2. Business/management
3. Biology/ecology
4. Physics/chemistry
5. Music/art/architecture
6. History/anthropology
7. Geology/astronomy (furthest — prefer)

Step 3: Import and Translate

From each donor domain, extract the specific mechanism that makes the shape work there. Then translate it into the dream seed's domain.

Translation checklist:

• What is the equivalent of [donor element] in [seed domain]?
• What constraint in the donor domain doesn't exist in ours? (Remove it)
• What constraint in our domain doesn't exist in the donor? (Add it)
• What emergent property does the donor mechanism produce? (Can we get that too?)

See references/translation-patterns.md for failure modes and annotated examples.

Step 4: Generate Synthesis Seeds

Compose 3-5 new seeds that carry the imported mechanism. Tag each with [SYNTHESIS:donor→target] for lineage tracking.

Format:

[SYNTHESIS:geology→memory] <new seed importing geological mechanism into memory>

Store via append_to_state

Use append_to_state with key dream_seeds to add each synthesis seed.

Quality Checks

Before storing, verify each synthesis seed:

• Imports a mechanism (how something works), not a metaphor (what something looks like)
• The donor domain is at least 3 steps away on the distance ranking
• The translated mechanism produces a genuinely novel structural pattern
• Someone from the donor domain would recognize the mechanism
• Someone from the target domain would find it surprising

See Also

• references/domain-shape-library.md — 20 structural shapes with donor domains
• references/translation-patterns.md — failure modes and annotated examples