| name | lite-constraints |
| description | Generates synthesis-constraints.json for the Diffmode growth-tactics pipeline by reasoning in-context over the per-run growth-factors.json (LIGHT vector DB) + founder context — the clean-room, no-Python replacement for the proprietary Python constraints generator. Emits the white-space pairs, mandatory synergy/founder-leverage pools, prohibited (conventional-outcome) combinations, and category-diversity requirements that the synthesis chain reads. Use after growth-factors mining and before synthesis. |
| metadata | {"version":"1.0.0"} |
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)
-
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").
-
mandatory_combinations (explore + build read these pools) — a flat array; each
item has a pool, vectors, reason, priority:
- Pool A —
A_white_space (5): the strongest 5 from diverse_white_space.
- Pool B —
B_synergy (5): high-synergy CROSS-CATEGORY pairs (e.g. Structural +
Resource, Leverage + Positioning, Psychological + Leverage, Structural + Psychological,
Resource + Positioning). reason: name the synergy type.
- Pool C —
C_founder_leverage (5): pairs that exploit what THIS founder HAS
that's rare — their unfair advantages (technical skill, industry access, network,
domain expertise, existing audience) — within their time and budget. Both vectors
should be transferability "High"; at least one "Emerging". reason: name the asset
it exploits.
priority: must_include for A/B, suggested for C.
-
prohibited_combinations (Step 1's conventional-detection) — category/theme-level
rules that always yield conventional outcomes, each { "pattern": "<theme A> + <theme B>", "vectors_if_present": [<any matching ids from this run, or []>], "reason": "<the conventional mechanism it produces>", "alternative": "<keep one vector, swap the other for an emerging/contrarian one>" }. Always include these generic conventional patterns
(map them onto whatever matching vectors exist this run):
- "customer-research/personal-need" + "content/SEO flywheel" → "talk to customers and
write content" (CONVENTIONAL).
- "build-interesting/demo" + "platform-timing/share" → "build demos and share them".
- "behavioral-cohort/analytics" + "content funnel" → "segment users and optimize".
- "community-join" + "audience-borrowing" → "join communities and participate".
- "content flywheel" + any SEO/authority/backlink vector → "create content to rank".
Also emit (lite versions of the script's other fields, used loosely by synthesis explore/build):
-
anti_patterns (synthesis-build reads this INSTEAD of the proprietary anti-vector tracking) — a
short generic list of demand-gen anti-patterns to reject, each { "pattern": "...", "severity": "HIGH|MEDIUM", "why": "..." }. Always include: fabricated scarcity/urgency;
multi-account astroturfing / fake social proof; fake-door pages for nonexistent
features; product-dev-disguised-as-marketing (Day-1 is engineering); targeting existing
users for upgrades (retention, not acquisition); teaching customers to DIY the product
(cannibalization).
-
unconventional_anchors — 4-6 of the most "Emerging", high-transferability vectors
from this run, each { "vector": id, "category": "...", "good_partners": [ids], "avoid_partners": [ids of conventional/over-represented vectors], "reason": "..." }.
-
validation_rules — { "min_white_space": 5, "min_synergy_pairs": 5, "max_from_same_anchor": 2, "unconventional_target_pct": 50 }.
Output schema (write EXACTLY this JSON shape)
{
"version": "lite-1.0",
"source_note": "Built in-context from the per-run growth-factors.json LIGHT DB. NOT the proprietary intelligence layer.",
"generated_for": { "budget_max": 0, "team_size": "solo|small|full|unknown", "unfair_advantages": [], "hours_per_week": 0, "stage": "..." },
"diverse_white_space": [ { "vectors": ["id_a","id_b"], "reason": "...", "source": "white_space" } ],
"diverse_white_space_stats"
Procedure
- Parse founder constraints from
founder-input.md into generated_for (budget_max
from "Monthly marketing budget"/MRR; team_size from solo/team; unfair_advantages from
"Rare assets"; hours_per_week from "Hours per week for growth"; stage from metrics).
- Load the vectors from
growth-factors.json. Note category spread and which vectors
are Emerging / High transferability (white-space + anchor candidates) vs over-represented
(content-flywheel-like → exclude from white space).
Degraded-DB check: if the vectors collapse into fewer than 5 distinct mechanism
verbs (e.g. 8 of 20 vectors are "publish/write/create"), or a single category holds
70% of vectors, mark "db_quality": "degraded" in the output metadata and widen the
pools — relax the "Emerging" requirement for Pool C and allow one extra pair per pool
from adjacent categories. This is a warning that changes behavior, not a gate that halts.
- Build
diverse_white_space (8-10 cross-category pairs; run each mentally through the
"would a marketer say 'that's unusual'?" test; exclude content-flywheel-type vectors).
- Build the three
mandatory_combinations pools (A/B/C as above), using ONLY this
run's IDs. Respect max_from_same_anchor: 2.
- Build
prohibited_combinations (the generic patterns above, mapped onto matching
run IDs where they exist).
- Build
anti_patterns, unconventional_anchors, validation_rules.
- Compute
category_diversity_requirements from metadata.category_counts.
- Write valid JSON. Confirm it parses and every referenced ID exists in
growth-factors.json.
Validation checklist (self-check before returning)