lite-constraints

Lite Constraints (synthesis constraints, no Python)

You produce synthesis-constraints.json — the file the synthesis chain reads to force unconventional vector combinations and block conventional ones. In the paid pipeline this is generated by the proprietary Python constraints generator reading the proprietary intelligence layer (curated anchors + internal pair-scoring). Here you reason in-context over the per-run growth-factors.json + founder constraints to emit the same JSON shape. You drop the proprietary intelligence-layer scoring entirely — it was scaffolding for how the script picked pairs, not a field the synthesis prompts consume.

⚠️ Clean-room rule

Build constraints only from this run's growth-factors.json + founder-input.md. Do NOT read anything under tactics_DB/. Every vector ID you reference MUST exist in this run's growth-factors.json — never invent IDs and never use IDs you remember from the proprietary DB. Prohibited-combination rules are expressed at the category / theme level plus generic conventional-outcome patterns (clean-room), not as proprietary specific pairs.

Inputs & Output

• INPUT (required): WS/03-think-tanks/demand-generation/growth-factors.json (the LIGHT DB — read its vectors and metadata.category_counts).
• INPUT (required): WS/01-diagnostics/founder-input.md (budget, team size, hours, unfair advantages, stage — to build the founder-leverage pool and bias selection).
• OUTPUT: write WS/03-think-tanks/demand-generation/synthesis-constraints.json.

What the synthesis chain actually uses (build these fields)

1. diverse_white_space (explore + build read this) — 8-10 cross-category vector PAIRS that are genuinely unconventional and exclude over-represented "content flywheel"-type vectors. Each: { "vectors": [id_a, id_b], "reason": "<the emergent angle in plain English>", "source": "white_space" }. Pick pairs whose mechanisms, when combined, would make a generic marketer say "that's unusual / risky" (not "obviously do that").