content-scorer

# Content Scorer Skill Score any piece of marketing copy in seconds. Get a 0-100 resonance score, dimension-by-dimension breakdown, and specific rewrite suggestions — before you post, send, or publish. ## Free vs Premium **Free tier (no API key needed):** - `--demo` — run a full score on built-in demo copy, zero external calls, see exactly what the output looks like - `--compliance-only` — fast forbidden word scan, runs locally, no API - Score up to 3 pieces of copy/day using local MLX (if you have it running) **Premium tier (ANTHROPIC_API_KEY):** - Unlimited scoring via Claude Haiku (~$0.001 per score) - `--rewrite` — get improved copy alongside your score - `--compare` — A/B test multiple hooks side-by-side - `--format=json` — pipe scores into your agent workflows - Batch scoring for content calendars The free compliance check alone is worth installing — catch forbidden words before they go live. ## What this skill does Analyzes marketing copy across 6 weighted dimensions and returns: - **Content Resonance Score (0-100)** — composite score calibrated against fMRI brain-response patterns (TRIBE v2 weight calibration) - **Per-dimension scores** — hook strength, specificity, emotional resonance, NLP technique usage, CTA strength, compliance - **Rewrite suggestions** — specific line-level changes to improve the weakest dimensions - **Platform fit check** — flag copy that's too long/short for the target platform - **Compliance gate** — detect forbidden words before they go live ## Scoring dimensions | Dimension | Weight | What it measures | |---|---|---| | Hook Strength | 25% | First line/sentence — does it grab attention in <3 seconds? | | Emotional Resonance | 25% | Does it connect to the reader's real situation, fear, or desire? | | NLP Technique Usage | 20% | Presuppositions, embedded commands, pacing/leading, reframes, future pacing | | Specificity | 15% | Concrete numbers, outcomes, timeframes — no vague platitudes | | CTA Strength | 10% | Clear, urgent next step with no exit ramp | | Compliance | 5% | No forbidden words, MLO-safe language | **Why these weights:** TRIBE v2 fMRI analysis found hook + emotional resonance drive 50% of cortical engagement in language and reward circuits. NLP technique presence activates anterior insula (urgency) and mPFC (social motivation). Specificity activates hippocampal encoding — specific claims are better remembered. ## Input contract Tell me: 1. **The copy to score** — paste it directly 2. **Platform** (optional): email / linkedin / x / facebook / instagram / sms / ad / script / any 3. **Audience** (optional): first-time buyers / investors / realtors / general 4. **Rewrite mode** (optional): `--rewrite` to get revised copy alongside the score Example prompts: - "Score this LinkedIn post: [paste copy]" - "Score for email, real estate investors: [paste copy]" - "Score and rewrite: [paste copy] --rewrite" - "Compliance check only: [paste copy]" - "Score these 3 hooks and tell me which is strongest: [hook A] / [hook B] / [hook C]" ## Output contract **Standard score output:** ``` Content Resonance Score: 74/100 Dimension Breakdown: Hook Strength: 8/10 ✓ Strong pattern interrupt Emotional Resonance: 7/10 ✓ Connects to ownership aspiration NLP Technique: 6/10 → Pacing present, no embedded command Specificity: 8/10 ✓ Concrete price + timeline CTA Strength: 5/10 ⚠ Exit ramp: "if you're interested" Compliance: 10/10 ✓ Clean Weakest point: CTA exit ramp — "if you're interested" gives reader a way out. Top suggestion: Replace "if you're interested, DM me" with "Drop your zip below — I'll pull your numbers." NLP detected: pacing_leading ("Most buyers in your area right now..."), future_pacing ("Picture yourself...") Missing: embedded_command — add one imperative buried in declarative: "...which is why serious buyers are locking in now." ``` **Rewrite output** (with `--rewrite`): ``` [Score block above] --- REWRITE --- [Revised copy with changes highlighted] --- END REWRITE --- Changes made: 1. Hook → stronger pattern interrupt (removed "I'm going to share...") 2. CTA → assume-the-close ("Drop your zip below" instead of "if you're interested") 3. Added embedded command in body ("...smart buyers are locking in this week") ``` **Multi-hook comparison:** ``` Hook A: 6/10 — Generic opener, no pattern interrupt Hook B: 9/10 — Strong curiosity gap + specificity ("Most buyers don't know this costs them $340/month") Hook C: 7/10 — Emotional but vague, lacks specificity Winner: Hook B. Combines curiosity gap with concrete loss framing. ``` ## How the skill works Uses `score_content.py` (in this directory). Local MLX first (`LLM_BACKEND=local`), Haiku fallback. ```bash # Score a piece of copy python3 score_content.py "Your LinkedIn post text here" --platform=linkedin # Score + rewrite python3 score_content.py "Your copy here" --platform=email --rewrite # Compare hooks python3 score_content.py --compare "Hook A text" "Hook B text" "Hook C text" # Compliance check only (fast, no API call needed) python3 score_content.py "Your copy" --compliance-only # JSON output (for agent pipelines) python3 score_content.py "Your copy" --format=json | jq '.score' # Force backend LLM_BACKEND=local python3 score_content.py "copy" # Qwen3.5-9B (free) LLM_BACKEND=haiku python3 score_content.py "copy" # Claude Haiku (~$0.001/score) ``` **Core scoring implementation:** ```python SCORING_PROMPT = """You are a direct-response copywriting analyst trained in: - Hormozi (value stacking, urgency, no-brainer offers) - Belfort straight-line persuasion (tonality, certainty, trust) - Cardone 10X (boldness, assumptive language, commitment) - NLP persuasion (presuppositions, embedded commands, pacing/leading, reframes, future pacing) Score the following {platform} copy on a 0-10 scale for each dimension. Be strict — a 10 means the best direct-response copy you've ever seen. COPY TO SCORE: {copy} AUDIENCE: {audience} Respond ONLY in this JSON format: {{ "hook_strength": {{ "score": N, "reason": "...", "improvement": "..." }}, "emotional_resonance": {{ "score": N, "reason": "...", "improvement": "..." }}, "nlp_technique": {{ "score": N, "detected": ["technique1", ...], "missing": "...", "improvement": "..." }}, "specificity": {{ "score": N, "reason": "...", "improvement": "..." }}, "cta_strength": {{ "score": N, "reason": "...", "improvement": "..." }}, "compliance": {{ "score": N, "violations": [] }}, "overall_comment": "..." }}""" WEIGHTS = { "hook_strength": 0.25, "emotional_resonance": 0.25, "nlp_technique": 0.20, "specificity": 0.15, "cta_strength": 0.10, "compliance": 0.05, } FORBIDDEN_WORDS = [ "pre-approval", "pre-approved", "pre-qualify", "specialist", "mortgage", "lending", "rates", "loan", "showings", "tours", "transfer", "connect", "team", "agent", "department", "qualify for", "AWESOME" ] def compliance_check(copy: str) -> list[str]: """Fast local check — no API call needed.""" violations = [] copy_lower = copy.lower() for word in FORBIDDEN_WORDS: if word.lower() in copy_lower: violations.append(word) return violations def composite_score(dimensions: dict) -> int: total = sum(dimensions[k]["score"] * WEIGHTS[k] for k in WEIGHTS) return round(total * 10) # 0-100 async def score(copy, platform="any", audience="general", rewrite=False): violations = compliance_check(copy) prompt = SCORING_PROMPT.format(copy=copy, platform=platform, audience=audience) response = await client.messages.create( model="claude-haiku-4-5-20251001", max_tokens=1024, messages=[{"role": "user", "content": prompt}] ) result = json.loads(response.content[0].text) result["compliance"]["violations"] = violations result["compliance"]["score"] = 10 if not violations else max(0, 10 - len(violations) * 3) result["composite"] = composite_score(result) if rewrite and result["composite"] < 85: result["rewrite"] = await generate_rewrite(copy, result, platform, audience) return result ``` ## Calibration note — TRIBE v2 Dimension weights are calibrated against TRIBE v2 (Meta's fMRI brain-response prediction model, `facebook/tribev2`). Emma sales call transcripts were run through TRIBE to measure predicted neural activation in language (STG/IFG), reward (mPFC/precuneus), and urgency (ACC/anterior insula) circuits. Calibration findings: - **Hook + emotional resonance → 50% of language/reward activation** (hence 25% + 25% weights) - **NLP techniques → anterior insula / urgency circuit activation** (20% weight) - **Specificity → hippocampal encoding** — concrete claims stick (15% weight) - **CTA framing → frontal-pole decisional activation** (10% weight) To recalibrate weights with fresh TRIBE data: see `vault/learnings/2026-03-27-tribe-v2-colab-spec-task47.md`. ## Use cases by role **Sales copy (pre-send):** "Score this email sequence — I'm targeting homebuyers who browsed last week" **Social content (pre-post):** "Score this LinkedIn post and tell me if the hook is strong enough" **Hook A/B testing:** "Which of these 3 hooks will perform better and why?" **Compliance pre-check:** "Check this for forbidden words before I post it" **Training data QA:** "Score Turn 3 of this Emma call transcript for NLP technique usage" ## Integration with agent infrastructure ```bash # Via Telegram @openclaw content-scorer "Score this email: [paste]" @openclaw content-scorer "Compare hooks: [hook A] / [hook B]" # Via Claude Code openclaw run content-scorer "Score for LinkedIn: [paste copy]" # In agent pipelines (JSON mode) python3 score_content.py "[copy]" --format=json | jq '.composite' ``` ## Benchmark scores (reference) | Copy type | Typical range | Notes | |---|---|---| | Generic real estate post | 40-55 | Vague, no hook, weak CTA | | Good LinkedIn post | 60-75 | Decent hook, some specificity | | Emma Turn 3 (post-R15) | 72-85 | Strong NLP, assume-the-close CTAs | | Direct response ad (top 5%) | 85-92 | Hormozi-style, concrete, urgent | | Perfect score territory | 93-100 | Rarely seen — Claude Sonnet 4.6 + expert copy review |