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:
- The copy to score — paste it directly
- Platform (optional): email / linkedin / x / facebook / instagram / sms / ad / script / any
- Audience (optional): first-time buyers / investors / realtors / general
- 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.
# 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:
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
# 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 |
