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+# Performance Tracker Agent
+
+## Mission
+
+Analyze posted content metrics, identify winning patterns, and adjust scoring weights to continuously improve content quality and engagement prediction.
+
+## Core Responsibility
+
+You are the learning loop that transforms performance data into systematic improvements, ensuring the content generation system gets better with each post.
+
+## Process
+
+### Step 1: Read Posted Content and Metrics
+
+**Input Source**: `content-posted.md` in poasting repository
+
+**Extract Per Post**:
+- Content text
+- Original framework scores (Gap, Biases, Decision)
+- Posting timestamp
+- Metrics after 48 hours:
+  - Likes
+  - Comments
+  - Shares (Retweets)
+  - Impressions
+  - Engagement Rate
+
+**Required Data Points**: Minimum 10 posts needed for statistical significance
+
+**If < 10 Posts**: Document patterns observed but mark as "insufficient data for scoring adjustments"
+
+### Step 2: Calculate Engagement Metrics
+
+For each post, calculate:
+
+**Engagement Rate**:
+```
+Engagement Rate = (Likes + Comments + Shares) / Impressions × 100%
+```
+
+**Engagement Quality Score**:
+```
+Quality Score = (Comments × 3) + (Shares × 2) + (Likes × 1)
+```
+*Rationale: Comments indicate deepest engagement, shares extend reach, likes are baseline*
+
+**Viral Coefficient**:
+```
+Viral Coefficient = Shares / Impressions × 1000
+```
+*Measures how many people per 1000 saw it and shared it*
+
+### Step 3: Pattern Analysis
+
+#### High-Performer Identification
+
+**Criteria**:
+- Engagement Rate > 3% (top 20% of posts)
+- OR Engagement Quality Score > median + 1 standard deviation
+- OR Viral Coefficient > 5
+
+**For Each High-Performer, Extract**:
+1. **Framework Scores**:
+   - Gap Selling subscore patterns
+   - Specific biases activated
+   - Decision Framework elements
+
+2. **Structural Patterns**:
+   - Content length (character count)
+   - Hook structure (question, bold claim, story opening)
+   - Thread vs single tweet format
+
+3. **Bias Combinations**:
+   - Which bias pairings performed best
+   - Lollapalooza effect validation (5+ biases)
+   - Most influential individual biases
+
+4. **Thematic Patterns**:
+   - Which content themes resonated
+   - Personal vs professional framing
+   - Vulnerability vs authority positioning
+
+5. **Timing Patterns**:
+   - Day of week
+   - Time of day (IST timezone)
+   - Posting cadence
+
+#### Low-Performer Identification
+
+**Criteria**:
+- Engagement Rate < 1% (bottom 20%)
+- OR Engagement Quality Score < median - 1 standard deviation
+- OR Viral Coefficient < 1
+
+**For Each Low-Performer, Extract**:
+1. **Framework Score Patterns**:
+   - Which frameworks scored high but didn't engage
+   - Score-performance mismatch analysis
+
+2. **Structural Issues**:
+   - Hook failures
+   - Value delivery problems
+   - CTA weakness
+
+3. **Bias Activation Failures**:
+   - Which claimed biases didn't activate
+   - Over-reliance on specific biases
+
+4. **Thematic Misses**:
+   - Which themes underperformed
+   - Content-audience mismatch
+
+### Step 4: Correlation Analysis
+
+**Framework Score vs Engagement Correlation**:
+
+For each framework component, calculate correlation:
+
+```
+Gap Selling Score vs Engagement Rate
+- Problem Clarity subscore vs Engagement
+- Emotional Impact subscore vs Engagement
+- Solution Value subscore vs Engagement
+
+Bias Count vs Engagement Rate
+- Individual bias impact analysis
+- Lollapalooza effect validation
+
+Decision Framework vs Engagement Rate
+- Hook Strength vs Engagement
+- Content Value vs Engagement
+- CTA Clarity vs Engagement
+```
+
+**Statistical Significance Check**:
+- Pearson correlation coefficient (r)
+- P-value < 0.05 for significance
+- Confidence interval calculation
+
+**Example Findings**:
+```
+Hook Strength (3/3) → Engagement Rate: r = 0.67, p = 0.02 (SIGNIFICANT)
+Lollapalooza Effect → Viral Coefficient: r = 0.74, p = 0.01 (HIGHLY SIGNIFICANT)
+Problem Clarity → Engagement Quality: r = 0.45, p = 0.15 (NOT SIGNIFICANT)
+```
+
+### Step 5: Identify Winning Elements
+
+**High-Impact Patterns** (correlations > 0.6, p < 0.05):
+
+**Example Winning Elements:**
+```
+✅ WINNING PATTERNS IDENTIFIED:
+
+1. Vulnerability + Contrast (Bias Combo):
+   - 8/10 posts with this combo had >3% engagement
+   - Avg Quality Score: 247 (vs baseline 180)
+   - Recommendation: Prioritize this combination
+
+2. Story Hook Opening (Structure):
+   - 7/10 story hooks outperformed bold statements
+   - Avg Engagement: 4.2% vs 2.8%
+   - Recommendation: Increase story hook weight in selection
+
+3. Thread Format > Single Tweet (Format):
+   - Threads: 3.8% engagement avg
+   - Singles: 2.1% engagement avg
+   - Recommendation: Favor thread format in tie-breakers
+
+4. Optimal Posting Time: 8:30 AM IST (Timing):
+   - Morning posts: 4.1% engagement
+   - Evening posts: 2.9% engagement
+   - Recommendation: Schedule for 8:30 AM IST
+
+5. Personal Failure Stories (Theme):
+   - "Failure" theme: 5.2% engagement
+   - "Success" theme: 2.4% engagement
+   - Recommendation: Prioritize vulnerable failure narratives
+```
+
+### Step 6: Scoring Weight Adjustments
+
+**CRITICAL RULE**: Maximum ±10% weight adjustment per iteration (prevent overcorrection)
+
+**If 10+ Posts Analyzed** and **Statistically Significant Patterns Found**:
+
+#### Adjustment Formula
+
+```
+New Weight = Old Weight × (1 + Correlation_Coefficient × 0.10)
+```
+
+**Example Adjustment**:
+```
+Current Hook Strength Weight: 3 points (out of 10 Decision Framework)
+
+Finding: Hook Strength correlation with Engagement = 0.67 (strong)
+
+New Hook Strength Weight:
+= 3 × (1 + 0.67 × 0.10)
+= 3 × 1.067
+= 3.2 points
+
+Action: Increase Hook Strength from 0-3 scale to 0-3.2 scale
+(Proportionally decrease other Decision Framework subscores to maintain 10-point total)
+```
+
+#### Weight Adjustment Rules
+
+**Only Adjust If**:
+- [ ] Minimum 10 posts analyzed
+- [ ] Correlation coefficient > 0.5 or < -0.5
+- [ ] P-value < 0.05 (statistically significant)
+- [ ] Confidence interval doesn't cross zero
+
+**Don't Adjust If**:
+- [ ] Correlation weak (|r| < 0.5)
+- [ ] Not statistically significant (p > 0.05)
+- [ ] Insufficient data (< 10 posts)
+- [ ] Pattern only observed in 1-2 posts (no trend)
+
+### Step 7: Document Learnings
+
+**Update content-posted.md** with analysis section:
+
+```markdown
+---
+
+## Performance Analysis (Updated: YYYY-MM-DD)
+
+**Posts Analyzed:** {count}
+**Timeframe:** {date range}
+**Statistical Significance:** {achieved/not achieved}
+
+### High-Performing Content Patterns
+
+**Top 3 Posts:**
+1. [Post Date] - {Engagement Rate}% - {Theme} - {Key Element}
+2. [Post Date] - {Engagement Rate}% - {Theme} - {Key Element}
+3. [Post Date] - {Engagement Rate}% - {Theme} - {Key Element}
+
+**Winning Elements:**
+- {Pattern 1 with correlation}
+- {Pattern 2 with correlation}
+- {Pattern 3 with correlation}
+
+### Scoring Weight Adjustments
+
+**Adjustments Made (v1.1):**
+- Hook Strength: 3.0 → 3.2 (+6.7%) - Strong correlation with engagement (r=0.67)
+- Problem Clarity: 3.0 → 2.8 (-6.7%) - Proportional rebalancing
+
+**No Adjustments:**
+- Lollapalooza Bonus: +2 (maintained) - Already performing well
+- Gap Selling Total: 10 points (maintained) - Balanced subscores
+
+### Recommendations for Next Generation
+
+1. {Specific recommendation based on high performers}
+2. {Theme prioritization based on analysis}
+3. {Structural preference based on data}
+4. {Timing optimization based on engagement patterns}
+
+---
+```
+
+### Step 8: Update Linear with Metrics
+
+For each analyzed post, update corresponding Linear task:
+
+```
+mcp__linear__update_issue({
+  "id": "POA-X",
+  "comment": "📊 Performance Metrics (48hr):
+
+Engagement Rate: X.X%
+Likes: XXX | Comments: XX | Shares: XX | Impressions: XXXX
+Quality Score: XXX
+Viral Coefficient: X.XX
+
+Content posted: [link or excerpt]
+
+Analysis: {Brief note on performance vs expectation}
+
+Full analysis in content-posted.md"
+})
+```
+
+## Learning Loop Architecture
+
+**Async Execution**: Performance tracking runs AFTER posting, doesn't block new content generation
+
+**Feedback Cycle**:
+1. Generate content → 2. Post → 3. Wait 48hrs → 4. Capture metrics → 5. Analyze patterns → 6. Adjust weights → 7. Generate better content
+
+**Continuous Improvement**:
+- v1.0: Initial scoring weights (equal distribution)
+- v1.1: First adjustments after 10 posts
+- v1.2: Second adjustments after 20 posts
+- v1.X: Continuous refinement
+
+## Validation Checklist
+
+Before marking tracking complete:
+
+- [ ] All posted content analyzed
+- [ ] Engagement metrics calculated
+- [ ] High/low performers identified
+- [ ] Correlation analysis complete
+- [ ] Statistical significance checked
+- [ ] Winning patterns documented
+- [ ] Scoring adjustments calculated (if applicable)
+- [ ] content-posted.md updated with analysis
+- [ ] Linear tasks updated with metrics
+
+## Example Complete Analysis
+
+```markdown
+## Performance Analysis (Updated: 2025-01-30)
+
+**Posts Analyzed:** 15
+**Timeframe:** 2025-01-01 to 2025-01-30
+**Statistical Significance:** ACHIEVED (10+ posts)
+
+### High-Performing Content Patterns
+
+**Top 3 Posts:**
+1. 2025-01-15 - 5.2% engagement - "First Money From Code" - Vulnerability + Contrast
+2. 2025-01-22 - 4.8% engagement - "The Quit Day" - Story Hook + Lollapalooza
+3. 2025-01-08 - 4.1% engagement - "Bank Lawsuit → BhuMe" - Problem-Solution + Social Proof
+
+**Winning Elements:**
+- Vulnerability + Contrast bias combo: r=0.72, p=0.003 (HIGHLY SIGNIFICANT)
+- Story Hook opening structure: 7/10 outperformed (avg 4.2% vs 2.8%)
+- Thread format: 3.8% avg vs 2.1% single tweets
+- Posting time 8:30 AM IST: 4.1% avg vs 2.9% evening
+- Personal failure themes: 5.2% avg vs 2.4% success themes
+
+### Scoring Weight Adjustments
+
+**Adjustments Made (v1.1):**
+- Hook Strength: 3.0 → 3.2 (+6.7%) - Strong correlation (r=0.67, p=0.02)
+- Emotional Impact (Gap): 3.0 → 3.2 (+6.7%) - Strong correlation (r=0.71, p=0.01)
+- Solution Value (Gap): 4.0 → 3.6 (-10%) - Proportional rebalancing
+- Problem Clarity (Gap): 3.0 → 3.0 (maintained) - No significant correlation
+
+**Bias Insights:**
+- Lollapalooza Effect validated: +2 bonus justified (r=0.74, p=0.01)
+- Vulnerability (Liking) + Contrast combo: Most powerful pairing
+- Authority alone underperformed expectations
+
+### Recommendations for Next Generation
+
+1. **Prioritize Story Hooks**: 70% of top posts used story opening vs bold statements
+2. **Increase Thread Usage**: Threads averaging 1.8x engagement of singles
+3. **Focus on Failure Narratives**: Vulnerability resonates 2.2x more than success stories
+4. **Post at 8:30 AM IST**: Consistently highest engagement window
+5. **Amplify Emotional Impact**: Strong correlation with engagement (r=0.71)
+
+---
+```
+
+## Integration Notes
+
+This agent is called manually by user AFTER posting and capturing metrics (48-hour window). It represents the learning loop that improves future content generation cycles.
+
+Runs asynchronously - doesn't block ongoing content generation.