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Continuously learns and evolves patterns from interactions

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SKILL.md

name DNA Evolution
description Continuously learns and evolves patterns from interactions
context fork
trigger auto
priority critical
alwaysActive true

DNA Evolution - Continuous Learning System

Mission

Capture patterns from interactions to evolve Arthur's Cognitive DNA profile. The goal is 1+1 = exponential — learned patterns compound across sessions rather than starting fresh.

Activation

Always active. Monitors interactions for learning signals and updates DNA profile.


Learning Triggers

Explicit Feedback

  • User says "that worked well" → Reinforce pattern
  • User says "don't do that" → Mark as anti-pattern
  • User rates session → Update feedback log
  • User expresses preference → Capture to preferences

Implicit Signals

  • Correction received → Pattern was wrong, adjust confidence
  • Approach succeeded → Reinforce with evidence
  • Same question repeated → Explanation wasn't clear
  • User simplified request → Original was over-engineered

Pattern Recognition

  • Technology choice praised → Strengthen preference
  • Methodology worked → Increase confidence
  • Integration succeeded → Document working pattern
  • Error resolved → Capture resolution pattern

DNA Profile Structure

Location: .claude/context/cognitive-dna/arthur-dna-profile.json

What Gets Updated

Section Update Trigger Update Type
strengths Positive feedback, successful pattern Increase confidence, add evidence
limitations Gap identified, failure pattern Adjust augmentation strategy
preferences.technical Technology success/failure Add/prioritize technologies
preferences.communication Clear/unclear feedback Adjust communication patterns
feedback.sessions End of significant work Append session record

Evolution Tracking

Every significant update creates a snapshot:

  • Location: .claude/context/cognitive-dna/snapshots/
  • Format: {timestamp}-{trigger}.json
  • Contains: Full profile at that point

Learning Actions

On Positive Feedback

# Pseudocode
pattern = identify_pattern(feedback)
if pattern in profile.strengths:
    profile.strengths[pattern].confidence += 0.01
    profile.strengths[pattern].evidence += 1
else:
    add_emerging_strength(pattern)

On Correction

# Pseudocode
wrong_pattern = identify_pattern(correction)
if wrong_pattern in profile.strengths:
    profile.strengths[wrong_pattern].confidence -= 0.02
if needs_augmentation(wrong_pattern):
    add_to_limitations(wrong_pattern)

On Session End

# Pseudocode
session = {
    "id": generate_id(),
    "timestamp": now(),
    "activity": summarize_session(),
    "highlights": extract_highlights(),
    "technologies": extract_technologies(),
    "rating": infer_or_ask_rating()
}
profile.feedback.sessions.append(session)

Gap Augmentation

When a limitation is detected, proactively compensate:

Gap Area Augmentation Strategy
Security Include OWASP Top 10, suggest security review
Performance Suggest profiling, recommend benchmarks
Testing Recommend TDD, provide test templates
Frontend Provide React/Vue patterns proactively
Rapid Prototyping Suggest time-boxed spikes

Integration with HETA

DNA patterns inform branch expert behavior:

Backend Expert

  • Apply: Sequential problem-solving, validation emphasis
  • Augment: Security considerations, performance profiling

Frontend Expert

  • Apply: Code examples, detailed explanations
  • Augment: React/Vue best practices (lower confidence area)

Infrastructure Expert

  • Apply: DevOps patterns (high confidence: 73.7%)
  • Augment: Security hardening

Commands

/dna-status

Show current DNA profile summary

/dna-learn {pattern}

Explicitly capture a new pattern

/dna-feedback {rating} {note}

Record session feedback

/dna-snapshot

Force create a snapshot of current profile


Persistence

On Every Significant Change

  1. Update arthur-dna-profile.json
  2. Create snapshot if major change
  3. Update important-context.md if critical

Snapshot Triggers

  • 5+ evidence points added to single strength
  • New limitation identified
  • Confidence shift > 5%
  • Explicit user request

Success Metrics

  • Patterns from session N improve session N+1
  • Corrections decrease over time
  • High-confidence areas produce consistently positive feedback
  • Augmented gaps show improvement

DNA Evolution ensures every interaction makes the next one better.