Agent Skills: Pattern-Learner Skill

Pattern-Learner Skill

Purpose

Self-improving pattern database that analyzes successful assets (score ≥95) to extract effective prompt language and abstract reusable patterns for the kerala-rage library.

When to Use

• When an asset achieves a score ≥95, to capture the "winning" prompt language.
• When needing to update the global pattern library with newly discovered best practices.
• When analyzing iterations to understand which specific changes drove compliance improvements.

Process

1. Diff current prompt vs previous attempts
2. Extract language that drove compliance improvement
3. Abstract reusable pattern from specific instance
4. Tag effectiveness (high/medium/low based on first-attempt success)
5. Update /docs/kerala-rage-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

1. Asset validates ≥95 → trigger learning
2. Extract patterns → update library
3. Next asset uses updated patterns
4. Success reinforces pattern (effectiveness++)
5. 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.