A/B Hook Predictor — v2.0.0

Know which hook wins before you run the test. Depth 3 upgrade: every prediction now includes a confidence interval, specific rewrite surgery for losers (not just warning flags), and cross-ICP scoring to find the best audience for any hook.

What's New in v2.0.0

• - Confidence intervals — 91/100 (±8) instead of just 91/100. CI derived from dimension variance: concentrated signals → shakier prediction; spread signals → reliable.
• Rewrite surgery — losing variants get specific fix instructions per ICP: not "add loss framing" but "add: 'Every month at 580 costs you $410 more than a buyer at 760.'"
• Cross-ICP scoring (--cross-icp) — score any hook against all 5 ICP profiles to find its best audience. A hook written for crypto-mortgage buyers might score higher for veterans.
• Quality gate (--min-score=N) — flag variants below threshold with ⛔ marker. Use in content pipelines.
• Margin analysis — neck-and-neck (<10pt gap) vs dominant winner (>25pt) changes the recommendation: run a real test vs ship immediately.

Free vs Premium

Free tier (no API key, no server needed):

• - --demo — full ranked comparison with rewrite surgery + cross-ICP table, zero API calls
• INLINECODE5 — score a single piece of copy against any ICP, with --rewrite and --cross-icp support
• INLINECODE8 — verify install
• All 5 ICP profiles: crypto-mortgage, credit-repair, va-loan, realtor-partner, first-time-buyer
• All depth-3 features (CI, rewrites, cross-ICP) run on rule-based scoring — no API key needed

Premium tier (contentresonancescorer.py backend loaded):

• - --variants — full dimension breakdown on up to 5 variants
• INLINECODE10 — structured output for agent pipelines
• Richer CRS-backed feature scoring (TRIBE v2–calibrated weights)
• Divergence detection between ICP-weighted score and CRS baseline

The free tier is fully functional. Install and use immediately.

What this skill does

Takes 2-5 content variants and a target ICP, scores each against that ICP's neural weight profile, and returns a ranked list with:

• - Composite resonance score (0-100) — weighted by which psychological levers matter most to that specific buyer
• Per-dimension flags — what's working, what's missing, specific fix suggestions
• Ranked winner — clear call on which variant to run and why

The key insight: the same content performs differently for different buyer types. "Keep your Bitcoin" scores 91/100 for a crypto-mortgage ICP and 34/100 for a credit-repair lead. This tool makes that gap visible before you spend money testing it.

ICP profiles available

CLI usage

CODEBLOCK0

variants.json format:
CODEBLOCK1

Demo output

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Scoring dimensions by ICP

Each ICP has a different neural weight profile — same dimensions, different multipliers:

Why profiles differ: Validated against ICP research and TRIBE v2 fMRI findings. The credit-repair buyer (shame-sensitive, loss-averse) responds differently than the crypto holder (gain-seeking, autonomy-driven). Using the wrong profile scores 40% lower on average.

Calibration note — TRIBE v2

Neural weight profiles are calibrated against TRIBE v2 (Meta's fMRI brain-response prediction model). The weight multipliers reflect predicted activation in:

• - Reward circuit (mPFC/precuneus): gain framing, identity alignment → crypto-mortgage, realtor-partner ICPs
• Amygdala/loss circuit: loss framing, urgency → credit-repair, first-time-buyer ICPs
• Language cortex (STG/IFG): simplicity, second-person → credit-repair, va-loan ICPs

To recalibrate with fresh data: see vault/learnings/2026-03-27-tribe-v2-colab-spec-task47.md.

Integration

CODEBLOCK3

Use cases

Before running paid ads:
"Which of these 3 Facebook hook variants will perform best for first-time buyers?"

Before sending email:
"Score these 2 subject lines against credit-repair leads — which one opens more?"

Content calendar optimization:
"Rank these 5 LinkedIn hooks for realtor-partner ICP before we schedule them"

Hook diagnosis:
"My ad isn't converting credit-repair leads — score it and tell me what's wrong"

Cross-ICP testing:
"Run all 5 ICPs against this hook — which buyer type will respond best?"