| name | pattern-learner |
| description | Self-improving pattern database. Analyzes successful assets (≥95/100) → extracts effective prompt language → abstracts reusable patterns → updates library automatically. |
Pattern-Learner Skill
Trigger
Asset scores ≥95/100
Process
- Diff current prompt vs previous attempts
- Extract language that drove compliance improvement
- Abstract reusable pattern from specific instance
- Tag effectiveness (high/medium/low based on first-attempt success)
- Update
/docs/northcote-asset-generation-patterns.md
Example Learning
Input:
- Asset 4 (Wattle + Beetle) scored 96/100 on first attempt
- Previous generic metallic prompts failed (opaque flat paint)
- Success prompt: "Faceted geometric surface with prismatic color shift green→gold→copper"
Extracted Pattern:
## Pattern: Metallic Iridescence (Asset 4, 96/100)
**Context:** Any metallic insect carapace rendering
**Effective Language:**
"Faceted geometric surface" + "prismatic color shift [color1→color2→color3]"
**Why It Works:**
Specifies viewing angle dependence (not flat metallic paint)
**Effectiveness:** HIGH (validated 1st attempt)
**Apply To:**
- Jewel beetles, metallic spiders, iridescent wings
Pattern Structure
## Pattern: [Name] (Asset [N], [Score]/100)
**Context:** [When to use this pattern]
**Effective Language:** [Exact prompt syntax]
**Why It Works:** [Technical explanation]
**Effectiveness:** [HIGH|MEDIUM|LOW]
**Apply To:** [Use cases]
**Avoid:** [Common failure modes]
Integration
Prompt-Composer: Queries pattern library before generation
Flash-Sidekick: consult_pro analyzes prompt diffs for pattern extraction
Auto-Validator: Scores trigger pattern learning
Self-Improvement Loop
- Asset validates ≥95 → trigger learning
- Extract patterns → update library
- Next asset uses updated patterns
- Success reinforces pattern (effectiveness++)
- Failure demotes pattern (effectiveness--)
Database Evolution
Week 1: 5 patterns (from Assets 1-2)
Week 2: 12 patterns (from Assets 3-6)
Week 4: 25+ patterns (self-reinforcing)
Result: Each new asset easier than previous due to pattern accumulation
Efficiency
Without Learning:
- Asset 10 requires same trial-error as Asset 1
- No institutional knowledge accumulation
With Learning:
- Asset 10 leverages 9 previous successes
- First-attempt success rate increases exponentially
Pattern library evolves with each success. System learns its own best practices.
