gh-rpiplewar-shipfaster-content-gen/commands/content-score-all.md

description

description
Apply automated 3-framework scoring to all content variations

Score All Content Variations

Mission

Apply automated framework-based scoring (Gap Selling + Munger Biases + Decision Framework) to all content variations in content-drafts.md, calculating total scores with detailed breakdowns.

Process

Follow the Scorer agent instructions (agents/scorer.md) to:

1. Read all content variations from content-drafts.md
2. Apply 3-framework scoring with mathematical formulas
3. Calculate total scores (out of 30)
4. Update content-drafts.md with complete score breakdowns

Execution Steps

Step 1: Read Content from content-drafts.md

Location: /home/rpiplewar/fast_dot_ai/poasting/content-drafts.md

Identify:

• All content variations awaiting scoring
• Look for [To be filled by Scorer agent] placeholders
• Extract content text and bias targeting info

Step 2: Score Each Variation

For each content piece, calculate:

Framework 1: Gap Selling (0-10 points)

• Problem Clarity (0-3): Is problem explicit and relatable?
• Emotional Impact (0-3): Is pain point vivid and resonant?
• Solution Value (0-4): Is future state compelling and actionable?

Framework 2: Cognitive Biases (0-10+ points)

• Count activated biases from Munger's 25
• Lollapalooza bonus: +2 if 5+ biases converge
• List specific biases detected

Framework 3: Decision Framework (0-10 points)

• Hook Strength (0-3): Does first line grab attention?
• Content Value (0-4): Are insights actionable and transferable?
• CTA Clarity (0-3): Is next step crystal clear?

Total Score = Gap (0-10) + Biases (0-10+) + Decision (0-10)

Step 3: Assign Pass/Fail Verdict

Quality Thresholds:

• < 20: ❌ FAIL (filter out)
• 20-24: ✅ PASS (needs improvement)
• 25-27: ✅ PASS (GOOD)
• 28-30: ✅ PASS (EXCELLENT)

Step 4: Update content-drafts.md

Replace [To be filled by Scorer agent] with:

**Scores:**
- Gap Selling: X/10 (Problem: X/3, Impact: X/3, Solution: X/4)
- Biases Activated: Y (List: Bias1, Bias2, Bias3...)
- Decision Framework: Z/10 (Hook: X/3, Value: X/4, CTA: X/3)
- **TOTAL: XX/30** {✅ PASS or ❌ FAIL}

Validation Checklist

Before marking scoring complete:

• All variations scored
• All subscores documented with reasoning
• Bias lists include specific bias names (not just count)
• Lollapalooza bonus applied where applicable (5+ biases)
• Pass/Fail verdicts assigned (< 20 = FAIL, ≥ 20 = PASS)
• content-drafts.md updated with complete scores
• Scoring formulas followed exactly per scorer.md

Example Output

✅ Scoring Complete

Variations Scored: 25 (5 themes × 5 variations)

Score Distribution:
- EXCELLENT (28-30): 3 pieces
- GOOD (25-27): 8 pieces
- PASS (20-24): 10 pieces
- FAIL (< 20): 4 pieces

Pass Rate: 84% (21/25)

Highest Scoring:
1. Theme: First Money From Code, Variation 1 (Bold Statement) - 28/30
2. Theme: The Quit Day, Variation 5 (Lollapalooza) - 27/30
3. Theme: Personal Pain → Product, Variation 2 (Story Hook) - 26/30

Output File: content-drafts.md (updated with all scores)

Next Step: Run /content-critic-review for quality feedback

Error Handling

If scoring logic unclear:

• Refer to detailed rubrics in agents/scorer.md
• Use examples as reference
• Document edge cases for future refinement

If scores seem inaccurate:

• Cross-check against manual evaluation
• Verify detection patterns
• Adjust formulas if systematic drift detected

Accuracy Targets

Goal: Within ±2 points (10% margin) of manual expert evaluation

If accuracy drifts:

• Review scoring logic against actual performance
• Adjust detection patterns
• Document refinements in scorer.md

Next Steps

After successful scoring:

1. Review score distribution in content-drafts.md
2. Run /content-critic-review to get improvement suggestions
3. Or continue with full pipeline if running /content-full-pipeline