Skill: drivenautoplex1/ab-predictor (version 2.0.0)

--- name: ab-predictor description: Predict which ad hook, email subject, or social post wins before you spend a dollar. Depth 3 — confidence intervals on every prediction, rewrite surgery with specific fix instructions per losing variant, cross-ICP scoring to find your best audience, and quality gate mode. Each buyer type has a different neural weight profile — the same hook scores 80/100 for crypto holders and 12/100 for generic traffic. version: 2.0.0 author: drivenautoplex1 price: 0 tags: - marketing - copywriting - advertising - real-estate - mortgage - crypto - direct-response - ab-testing - sales - content - social-media - email-marketing - lead-generation - defi - trading metadata: openclaw: requires: anyBins: - python3 emoji: "🧠" homepage: https://github.com/drivenautoplex1/openclaw-skills install: [] --- # 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 - `--text` — score a single piece of copy against any ICP, with `--rewrite` and `--cross-icp` support - `--version` — 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 (content_resonance_scorer.py backend loaded):** - `--variants` — full dimension breakdown on up to 5 variants - `--json` — 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 | ICP key | Buyer type | Top neural levers | |---|---|---| | `crypto-mortgage` | BTC/ETH holder buying a home without selling | Gain framing, identity alignment, specificity | | `credit-repair` | 500-680 score, shame-sensitive | Pacing/empathy, reframe, social proof | | `va-loan` | Veteran who doesn't know their full benefit | Direct tone, identity (earned this), loss (leaving it unused) | | `realtor-partner` | Agent who needs a lender they can trust | Reliability signals, social proof, B2B framing | | `first-time-buyer` | First home, rate-shocked, overwhelmed | Simplicity, urgency (program deadlines), monthly payment framing | ## CLI usage ```bash # Demo: 3 hooks ranked against crypto-mortgage ICP (no API key needed) python3 ab_predictor.py --demo # Score one piece of copy against a specific ICP python3 ab_predictor.py --text "You don't have to sell your BTC to buy a house." --product crypto-mortgage # Score with rewrite surgery (specific fix instructions if below 70) python3 ab_predictor.py --text "Buy a home today with great rates." --product va-loan --rewrite # Compare variants from a JSON file python3 ab_predictor.py --variants hooks.json --product va-loan # Compare with rewrite surgery for losers python3 ab_predictor.py --variants hooks.json --product credit-repair --rewrite # Find best audience for a hook across all ICPs python3 ab_predictor.py --variants hooks.json --cross-icp # Quality gate: flag anything below 60 python3 ab_predictor.py --variants hooks.json --product first-time-buyer --min-score 60 # JSON output for pipelines (includes CI, rewrite suggestions) python3 ab_predictor.py --variants hooks.json --product credit-repair --json --rewrite | jq '.[0]' # Check version python3 ab_predictor.py --version ``` **variants.json format:** ```json [ {"label": "Hook A — Direct benefit", "text": "You earned zero down. Here's how to use it."}, {"label": "Hook B — Loss frame", "text": "Every month you wait, another home goes under contract."}, {"label": "Hook C — Identity", "text": "Veterans are buying homes with $0 down. Here's the exact process."} ] ``` ## Demo output ``` $ python3 ab_predictor.py --demo A/B Resonance Comparison — ICP: crypto-mortgage ================================================ #1 Hook C — Identity + concrete 91/100 ✅ WINNER ✓ Identity alignment: BTC holders — specific, in-group signal ✓ Gain framing: "unrealized gains", "appreciate while you build equity" ✓ Concrete noun density: $200K-$2M, 2022, Fannie Mae ✓ Loss avoidance framing: "zero capital gains event, zero coins sold" → Add urgency signal — crypto-mortgage window may not stay open #2 Hook A — Loss frame 72/100 ✓ Second-person: "You don't have to sell..." ✓ Specificity: "No capital gains. No missed appreciation." ⚠ Missing: identity signal — crypto-mortgage ICP responds to in-group language ⚠ Low gain framing — tells them what to avoid but not what they get → Add a concrete outcome: "...while your BTC keeps appreciating" #3 Hook B — Generic 12/100 ❌ DO NOT RUN ✗ Third-person tone: "We offer..." — not second-person ✗ Compliance violation: "rates", "pre-approvals" — forbidden words ✗ No identity alignment for crypto-mortgage ICP ✗ Zero specificity — vague platitudes only → Rewrite from scratch. This won't convert the crypto-mortgage buyer. Predicted winner: Hook C by 19 points over Hook A. Key differentiator: Identity alignment + concrete specificity trigger reward circuit activation. ``` ## Scoring dimensions by ICP Each ICP has a different neural weight profile — same dimensions, different multipliers: | Dimension | crypto-mortgage | credit-repair | va-loan | realtor-partner | first-time-buyer | |---|---|---|---|---|---| | Gain framing | 1.8× | 1.2× | 1.0× | 1.1× | 1.3× | | Loss framing | 0.8× | 1.9× | 1.4× | 1.5× | 1.6× | | Identity alignment | 1.7× | 1.3× | 1.8× | 1.6× | 1.0× | | Urgency | 0.6× | 1.4× | 1.2× | 0.8× | 1.5× | | Social proof | 0.9× | 1.6× | 1.1× | 1.9× | 1.4× | | Simplicity | 0.8× | 1.7× | 1.3× | 1.0× | 1.8× | | Second-person | 1.2× | 1.5× | 1.6× | 0.9× | 1.4× | | Concrete nouns | 1.6× | 1.1× | 1.3× | 1.7× | 1.2× | **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 ```bash # Via Telegram @openclaw ab-predictor "Compare these hooks for a VA loan buyer: [A] / [B] / [C]" @openclaw ab-predictor "Which hook wins for credit-repair leads? [paste variants]" # Pipeline: score hooks, pick winner, pass to content-scorer for final polish python3 ab_predictor.py --variants hooks.json --product va-loan --json \ | jq -r '.[0].text' \ | xargs -I{} python3 ../content-scorer/score_content.py "{}" --platform=facebook # Batch: test same hooks across all 5 ICPs for icp in crypto-mortgage credit-repair va-loan realtor-partner first-time-buyer; do echo "=== $icp ===" && python3 ab_predictor.py --variants hooks.json --product $icp done ``` ## 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?"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)