learning-engine

learning-engine

System records mistakes and successes, automatically learns patterns to improve skills. Automates "don't repeat same mistake" principle.

Learning Sources

1. memory/errors/

Extract failure patterns from error logs

# memory/errors/2026-02-14.md
10:30 - insta-post failure

Cause: PNG file upload → "Problem occurred" error
Fix: Retry after JPG conversion → Success
Lesson: Always convert to JPG before Instagram upload

2. self-eval Results

Extract improvement points from weekly self-evaluation

# memory/self-eval/2026-W07.md
This Week's Mistakes

Too many browser snapshots (token waste)
→ Improvement: Call API directly via exec

This Week's Successes

95% token savings with insta-cli v2 DM check

3. performance Data

Learn successful/unsuccessful patterns from performance tracking

{
  "insight": "Posts at 7-9 PM get +30% likes",
  "rule": "Instagram posts recommended 19:00-21:00"
}

Auto Rule Generation

Convert learned patterns to rules:

Location:

memory/learned-rules/

memory/
  learned-rules/
    instagram-posting.md
    browser-automation.md
    api-usage.md
    error-recovery.md

Rule Format

# Instagram Posting Rules
Rule #1: Always Convert to JPG

Situation: Upload image to Instagram
Failure Pattern: PNG file → "Problem occurred"
Solution: 
convert input.png -quality 92 output.jpg
Evidence: 2026-02-10, 2026-02-14 error logs
Applied Skills: insta-post, cardnews, social-publisher

Rule #2: 1:1 Ratio Required

Situation: Instagram card news
Failure Pattern: 16:9 horizontal → Cropped in feed
Solution: Generate as 1024x1024 square
Evidence: 2026-02-13 feedback
Applied Skills: cardnews, nano-banana-pro

Inject Rules into Skills

Auto-add learned rules to relevant skill SKILL.md:

Location:

skills/{skill-name}/SKILL.md

# insta-post
...
Learned Lessons
Image Processing

✅ Always convert to JPG (PNG causes errors)
✅ 1:1 ratio required (1024x1024 recommended)
✅ File size < 8MB

Timing

✅ Posts at 19:00-21:00 get +30% engagement
❌ Avoid early morning posts

Automation

✅ Call API via exec (0 snapshots)
❌ Minimize browser automation

Weekly Learning Report

Auto-generated every Monday:

Location:

memory/learning/weekly-YYYY-Www.md

# 2026-W07 Learning Report
New Learnings (5)


Instagram PNG Ban

3 mistakes → Rule created
Applied: insta-post, cardnews



Token Saving: exec > Browser

v1: 5 snapshots → v2: 1 exec
95% savings



Optimal Posting Time

19:00-21:00 +30% likes



Brand Tone Effect

무펭이 tone +40% engagement



Auto Error Recovery

browser-dependent failure → Browser restart



Applied Skills

insta-post (2 rules)
cardnews (1 rule)
performance-tracker (1 insight)

Next Week Goals

 Build A/B testing system
 Add 3 auto-recovery patterns

Event Publishing

Publish event when learning complete:

Location:

events/lesson-learned-YYYY-MM-DD.json

{
  "timestamp": "2026-02-14T23:00:00Z",
  "source": "learning-engine",
  "new_rules": 2,
  "updated_skills": ["insta-post", "cardnews"],
  "summary": "Learned 2 Instagram image rules"
}

hook-engine Integration

• on-error hook: Error occurs → Record to memory/errors/ → learning-engine analysis
• post-hook (self-eval): After weekly evaluation → Update learning rules
• post-hook (performance): After collecting performance data → Learn patterns
• scheduled hook: Every Monday → Generate weekly learning report

Learning Pipeline

Error occurs
  ↓
Record to memory/errors/
  ↓
learning-engine analysis
  ↓
Extract patterns + Create rules
  ↓
Save to memory/learned-rules/
  ↓
Auto-update relevant skill SKILL.md
  ↓
Publish event (lesson-learned)
  ↓
Reflect in weekly report

Trigger Keywords

• "what did I learn"
• "learning"
• "lessons"
• "mistake patterns"
• "improvements"
• "learning report"
• "add rule"

Usage Examples

"What did I learn this week?"
→ Generate weekly learning report
"Organize Instagram posting mistake patterns"
→ Analyze memory/errors/ + Create rules
"Learn from performance data"
→ Extract successful patterns + Update rules

Auto-improvement Examples

Before (Pre-learning)

Instagram post fails → Manually convert to JPG → Retry
(Repeat every time)

After (Post-learning)

Execute insta-post → Auto-check/convert JPG → Success
(Rule injected into SKILL.md)

Meta Learning

learning-engine itself also learns:

• "Which rules are used most?"
• "Which skills improve most?"
• "Which areas have slow learning?"

Meta Learning Report:

memory/learning/meta-YYYY-MM.md

Future Improvements

• Rule conflict detection (Rule A vs Rule B)
• Rule confidence score (based on usage frequency)
• Auto A/B testing (rule validation)
• Share learning with other agents

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