content-retro

name	content-retro
description	Analyze content performance patterns. Extract what works (hook types, formats, personas). Auto-update learned defaults so future content improves.

/content-retro — The CMO's Feedback Loop

Analyze what's working across your published content. Extract winner patterns and auto-update the system's learned defaults so future content improves without manual tuning.

This is the skill that closes the loop. Everything upstream — /content-brief, /content-write, /content-gate — reads the defaults this skill updates.

Preamble

source "$(dirname "$0")/../lib/preamble.sh"

The Skill

Step 1: Load Performance Data

Read ~/.kai-marketing/content-log.jsonl and filter for pieces with performance_30d set (not null).

If fewer than 5 graded pieces exist, tell user: "Need 5+ graded pieces for reliable pattern extraction. You have {N}. Run /content-report to grade pending pieces, or publish more content."

Step 2: Pattern Analysis

Analyze the graded pieces for statistical patterns. For each dimension, compare winner rates:

Dimensions to analyze:

• Hook type: Which hook types (curiosity gap, social proof, pain agitate, contrarian, data-led) produce more winners?
• Format: Which formats (blog, linkedin, email, etc.) perform best?
• Persona: Which personas produce winners?
• Word count range: Is there a sweet spot?
• Publish day of week: Does timing matter?
• Quality gate score: What score range correlates with winners?

Statistical threshold: A pattern is significant only when:

• n >= 5 samples for that dimension value
• Winner rate delta >= 15% above baseline

Step 3: Display Findings

CONTENT RETRO — Pattern Analysis
══════════════════════════════════════════

Data: {N} pieces analyzed ({winners} winners, {avg} average, {under} underperformers)

SIGNIFICANT PATTERNS (n≥5, delta≥15%):

  Hook Type:
    curiosity_gap:  72% winner rate (n=7)  ← +22% above baseline
    social_proof:   40% winner rate (n=5)  ← baseline
    pain_agitate:   33% winner rate (n=6)  ← -17% below baseline

  Persona:
    Shock Absorber: 80% winner rate (n=5)  ← +30% above baseline
    Competent Cog:  50% winner rate (n=8)  ← baseline

  Format:
    blog:           65% winner rate (n=12) ← +15% above baseline

NOT ENOUGH DATA:
  - Publish day: need 5+ per day (max is 3 for Tuesday)
  - Word count: need 5+ per range

Step 4: Update Learned Defaults

For each significant pattern, propose updating the learned defaults:

"Found {N} significant patterns. Update learned defaults?"

If user approves:

1. Backup the current defaults: copy ~/.kai-marketing/marketing-defaults.md to ~/.kai-marketing/marketing-defaults.md.bak

2. Write updated defaults to ~/.kai-marketing/marketing-defaults.md:

# Learned Defaults — Auto-Generated by /content-retro
# Last updated: {date}
# Based on: {N} pieces analyzed

## Hook Preferences
- Prefer curiosity_gap hooks (72% winner rate, n=7)
- Avoid pain_agitate hooks when alternatives exist (33% winner rate, n=6)

## Persona Preferences
- Shock Absorber persona produces strongest results (80% winner rate, n=5)

## Format Preferences
- Blog format outperforms others (65% winner rate, n=12)

## Quality Floor
- Winners average gate score: {avg_winner_score}/100
- Minimum gate score for publish: {recommended_threshold}

3. Also write findings to ~/.kai-marketing/what-works.md (backup first):

# What Works — Pattern Archive
# Auto-updated by /content-retro

## {date} Analysis ({N} pieces)
{summary of findings}

Step 5: Confirm the Loop

Tell user: "Defaults updated. Next /content-brief and /content-write will use these patterns automatically."

Show the chain:

/content-retro just updated → marketing-defaults.md
  └→ /content-brief reads defaults → better briefs
      └→ /content-write reads defaults → better content
          └→ /content-gate scores → better pass rate
              └→ /content-report grades → more winners
                  └→ /content-retro analyzes → loop continues

Error Handling

• Not enough data (n<5): Show what data exists, suggest publishing more
• what-works.md corrupted: Restore from .bak if available, otherwise start fresh
• No performance data: Direct to /content-report first

Chain State

Reads from: ~/.kai-marketing/content-log.jsonl (graded entries) Writes to: ~/.kai-marketing/marketing-defaults.md, ~/.kai-marketing/what-works.md Read by: /content-brief, /content-write (next cycle)