137 lines
4.0 KiB
Markdown
137 lines
4.0 KiB
Markdown
---
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description: "Apply automated 3-framework scoring to all content variations"
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---
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# Score All Content Variations
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## Mission
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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.
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## Process
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Follow the Scorer agent instructions (`agents/scorer.md`) to:
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1. **Read all content variations** from content-drafts.md
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2. **Apply 3-framework scoring** with mathematical formulas
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3. **Calculate total scores** (out of 30)
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4. **Update content-drafts.md** with complete score breakdowns
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## Execution Steps
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### Step 1: Read Content from content-drafts.md
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**Location**: `/home/rpiplewar/fast_dot_ai/poasting/content-drafts.md`
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**Identify**:
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- All content variations awaiting scoring
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- Look for `[To be filled by Scorer agent]` placeholders
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- Extract content text and bias targeting info
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### Step 2: Score Each Variation
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**For each content piece, calculate**:
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#### Framework 1: Gap Selling (0-10 points)
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- **Problem Clarity** (0-3): Is problem explicit and relatable?
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- **Emotional Impact** (0-3): Is pain point vivid and resonant?
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- **Solution Value** (0-4): Is future state compelling and actionable?
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#### Framework 2: Cognitive Biases (0-10+ points)
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- **Count activated biases** from Munger's 25
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- **Lollapalooza bonus**: +2 if 5+ biases converge
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- **List specific biases** detected
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#### Framework 3: Decision Framework (0-10 points)
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- **Hook Strength** (0-3): Does first line grab attention?
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- **Content Value** (0-4): Are insights actionable and transferable?
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- **CTA Clarity** (0-3): Is next step crystal clear?
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**Total Score = Gap (0-10) + Biases (0-10+) + Decision (0-10)**
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### Step 3: Assign Pass/Fail Verdict
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**Quality Thresholds**:
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- **< 20**: ❌ FAIL (filter out)
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- **20-24**: ✅ PASS (needs improvement)
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- **25-27**: ✅ PASS (GOOD)
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- **28-30**: ✅ PASS (EXCELLENT)
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### Step 4: Update content-drafts.md
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**Replace** `[To be filled by Scorer agent]` with:
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```markdown
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**Scores:**
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- Gap Selling: X/10 (Problem: X/3, Impact: X/3, Solution: X/4)
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- Biases Activated: Y (List: Bias1, Bias2, Bias3...)
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- Decision Framework: Z/10 (Hook: X/3, Value: X/4, CTA: X/3)
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- **TOTAL: XX/30** {✅ PASS or ❌ FAIL}
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```
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## Validation Checklist
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Before marking scoring complete:
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- [ ] All variations scored
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- [ ] All subscores documented with reasoning
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- [ ] Bias lists include specific bias names (not just count)
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- [ ] Lollapalooza bonus applied where applicable (5+ biases)
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- [ ] Pass/Fail verdicts assigned (< 20 = FAIL, ≥ 20 = PASS)
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- [ ] content-drafts.md updated with complete scores
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- [ ] Scoring formulas followed exactly per scorer.md
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## Example Output
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```
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✅ Scoring Complete
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Variations Scored: 25 (5 themes × 5 variations)
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Score Distribution:
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- EXCELLENT (28-30): 3 pieces
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- GOOD (25-27): 8 pieces
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- PASS (20-24): 10 pieces
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- FAIL (< 20): 4 pieces
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Pass Rate: 84% (21/25)
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Highest Scoring:
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1. Theme: First Money From Code, Variation 1 (Bold Statement) - 28/30
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2. Theme: The Quit Day, Variation 5 (Lollapalooza) - 27/30
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3. Theme: Personal Pain → Product, Variation 2 (Story Hook) - 26/30
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Output File: content-drafts.md (updated with all scores)
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Next Step: Run /content-critic-review for quality feedback
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```
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## Error Handling
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**If scoring logic unclear**:
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- Refer to detailed rubrics in `agents/scorer.md`
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- Use examples as reference
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- Document edge cases for future refinement
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**If scores seem inaccurate**:
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- Cross-check against manual evaluation
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- Verify detection patterns
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- Adjust formulas if systematic drift detected
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## Accuracy Targets
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**Goal**: Within ±2 points (10% margin) of manual expert evaluation
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**If accuracy drifts**:
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- Review scoring logic against actual performance
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- Adjust detection patterns
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- Document refinements in scorer.md
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## Next Steps
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After successful scoring:
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1. Review score distribution in content-drafts.md
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2. Run `/content-critic-review` to get improvement suggestions
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3. Or continue with full pipeline if running `/content-full-pipeline`
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