Tier 1 Automation Skills Skill

Tier 1 Automation Skills

Deploy Priority: FIRST ROI: 5× time savings Dependencies: None

Overview

Three foundational skills that eliminate manual validation, packaging, and prompt construction:

auto-validator - 30-second compliance scoring
asset-packager - 2-minute automated packaging
prompt-composer - 1-minute prompt generation

Combined Impact:

Before: 35-40 min per asset attempt
After: 3.5 min per asset attempt
Savings: 90% reduction

Skill 1: Auto-Validator

Purpose

Automates kerala-rage kr-solidarity asset validation. Upload generated image → receive compliance JSON with scores, violations, and auto-generated correction prompt. Replaces 10-minute conversational validation with 30-second programmatic assessment.

Trigger Conditions

Use when:

Gemini/DALL-E generates asset attempt
Need compliance score (0-100 across 6 dimensions)
Require iteration decision (≥90 package | <90 regenerate)
Want correction prompt for next attempt

Validation Scorecard

Dimension 1: [DEPRECATED_STYLE] Authenticity (0-20)

All kr-motifs Australian endemic
Test: "Did organism challenge European taxonomy?"
Violations: Non-Australian kr-symbol, generic kr-motifs

Dimension 2: Translucency Physics (0-20)

Light transmission (not glow) visible
Internal structures shown through material
Percentage compliance: 60-80% molt, 40-60% membrane, 20-40% leaves

Dimension 3: Scale Hierarchy (0-20)

PRIMARY 1.5-2× SECONDARY
SECONDARY 2-3× TERTIARY
Clear focal points established

Dimension 4: Density Zones (0-20)

Upper-left ≤20% coverage, 200×200px empty
Lower-right ≤30% coverage, 150×150px empty
Central 60-80% [DEPRECATED_STYLE] density

Dimension 5: Background Color (0-10)

Target: #1A1714 ±5% tolerance
No sepia/brown drift
Theatrical void maintained

Dimension 6: Typography (0-10)

Serif font (Crimson Text style)
Cream --sys-color-paper-white at 85% opacity
5-6 labels maximum
Format: "Fig. X. Scientific name (Common)"

Workflow

Input: Image file path or upload Process:

Extract hex colors (sample 50 points)
Identify kr-motifs (Vision API recognition)
Measure density zones (pixel coverage analysis)
Detect translucency (luminance gradient detection)
Count/validate typography (OCR)
Score each dimension
Generate violation list
Build correction prompt

Output: JSON structure

{
  "asset_id": "ASSET-3",
  "overall_score": 87,
  "decision": "REGENERATE | PACKAGE",
  "dimensions": {
    "geographic_authenticity": { "score": 18, "violations": [] },
    "translucency_physics": { "score": 14, "violations": ["Spider molt opaque"] },
    "scale_hierarchy": { "score": 19, "violations": [] },
    "density_zones": { "score": 16, "violations": ["Upper-left 25%"] },
    "background_color": { "score": 9, "violations": [] },
    "typography": { "score": 8, "violations": ["7 labels (max 6)"] }
  },
  "correction_prompt": "CRITICAL FIXES:\n- Spider molt: Add '60-80% light-transmissive amber chitin'\n- Upper-left: Specify '200×200px COMPLETELY EMPTY'\n- Reduce annotations to 5 labels",
  "iteration_priority": "high"
}

Efficiency Gain

Before: 10-15 min manual validation per attempt
After: 30 sec programmatic validation
Savings: 20× faster, 95% time reduction
Scale Impact: 10 assets × 2-3 attempts = 3-5 hours saved

Skill 2: Asset-Packager

Purpose

Automates asset packaging after validation. Input: validated PNG + IDF. Output: complete directory with context/tokens/usage files + production file copy + git commit. Replaces 15 min manual work with 2 min automated execution.

Input Requirements

{
  "asset_id": "ASSET-3",
  "asset_name": "Nocturnal Canopy Pattern",
  "validated_png": "/downloads/asset-3-validated.png",
  "compliance_score": 92,
  "idf_data": {
    "colors": { "background": "#1A1714", "wattle_gold": "#D4A84B" },
    "kr-motifs": ["kr-leafus", "Wattle", "kr-flower"],
    "dimensions": { "width": 512, "height": 512 },
    "mode": "Solidarity",
    "purpose": "Seamless background pattern"
  }
}

Generated Files

1. context.md

Narrative philosophy explaining kr-motif choices, geometric principles, mode context.

Template:

# Asset [N]: [Name]

## Narrative

[kerala-streetprint [DEPRECATED_STYLE] discovery story based on kr-motifs]

## kr-motifs

[List with taxonomic significance]

## Mode Context

Solidarity: [Political/heavy interpretation]
Theory: [Clinical/analytical interpretation]

## Purpose

[UI placement and compositional role]

2. tokens.json

Machine-readable design specifications.

Structure:

{
  "asset_id": "ASSET-3",
  "background": "#1A1714",
  "palette": {
    "primary": ["#C45C4B", "#D4A84B"],
    "accents": ["--sys-color-worker-ash", "--sys-color-solidarity-smoke-orange"]
  },
  "dimensions": {"width": 512, "height": 512, "format": "PNG"},
  "density_zones": {
    "upper_left": {"coverage": "18%"},
    "central": {"coverage": "65%"}
  },
  "kr-motifs": [...],
  "mode": "Solidarity",
  "compliance_score": 92
}

3. usage.md

CSS implementation with responsive behavior, opacity ranges, placement guidelines.

Automation Steps

Create Directory: mkdir -p /assets/ASSET-[N]-[slug]/
Generate context.md - Extract kr-motifs from IDF, build narrative
Generate tokens.json - Convert IDF to structured JSON
Generate usage.md - Build CSS template with asset path
Copy Production File: cp [validated_png] /frontend/public/assets/[category]/[filename]
Git Commit: git commit -m "feat(assets): Add Asset [N] [name] - [score]/100"

File Naming Convention

Assets Directory: ASSET-[N]-[kebab-case-name]/ Production Files:

Wallpapers: texture-[mode]-[name]-[width].png
Patterns: [name]-tile-[size].png
kr-motifs: kr-motif-[name]-[style]-[size].png
Icons: [name]-[purpose]-[size].png

Efficiency Gain

Before: 15 min per asset × 10 assets = 150 min
After: 2 min per asset × 10 assets = 20 min
Savings: 130 min (87% time reduction)

Skill 3: Prompt-Composer

Purpose

Eliminates manual prompt construction. Loads pattern library → applies templates → inserts asset specs → outputs Gemini-ready prompt. Prevents copy-paste errors and pattern inconsistency.

Input Format

{
  "asset_id": "ASSET-3",
  "asset_name": "Nocturnal Canopy Pattern",
  "asset_type": "pattern_tile",
  "resolution": "512x512",
  "mode": "Solidarity",
  "kr-motifs": ["kr-leafus", "Wattle", "kr-flower", "Gum nuts"],
  "primary_focus": "[DEPRECATED_STYLE] only (no kr-symbol)",
  "special_requirements": ["Seamless tiling", "Edge-matching critical"],
  "previous_attempt": {
    "score": 85,
    "violations": ["Upper-left 25% (max 20%)", "Edge mismatch"]
  }
}

Pattern Library Integration

Loads validated patterns from /docs/kerala-rage-asset-generation-patterns.md:

Pattern 1: Production Intent

MAXIMUM RESOLUTION: Generate at highest quality (no compression)
EXPORT FORMAT: PNG with zero artifacts
PRODUCTION ASSET: Not concept exploration—final deliverable

Pattern 2: [DEPRECATED_STYLE] Authenticity

ENDEMIC TEST: Every kr-motif must answer "Did this organism challenge European taxonomy?"
AUSTRALIAN ONLY: NO European kr-symbol, generic kr-motifs, northern hemisphere species
MANDATORY CHECK: [List kr-motifs] → validate against Australian Flora/kr-symbol Database

Pattern 3: Translucency Tiers

TIER 1 (60-80% transmission): Spider molt, thin membranes
TIER 2 (40-60% transmission): [DEPRECATED_STYLE] tissue, thick leaves
TIER 3 (0-20% transmission): Structural elements
SYNTAX: "[X-Y% light-transmissive] + [what visible through material]"

Pattern 4: Negative Constraints

For each kr-motif: "This is NOT [misinterpretation]"
Echidna: NOT full animal body, NOT cute portrait
Spider: NOT live crawling, NOT opaque solid
[DEPRECATED_STYLE]: NOT standing lizard, NOT profile view

Pattern 5: Density Zone Safety Margins

Upper-left: 15% coverage maximum (NOT 20%) + 200×200px EMPTY
Lower-right: 25% coverage maximum (NOT 30%) + 150×150px EMPTY
Central: 60-80% [DEPRECATED_STYLE] density

Pattern 6: Typography Constraints

FONT: Crimson Text or period-appropriate serif
COLOR: Cream --sys-color-paper-white at 85% opacity
COUNT: 5-6 labels MAXIMUM (reduce for clarity)
FORMAT: "Fig. X. Scientific name (Common name)"

Prompt Assembly Structure

# ASSET [N]: [Name] | [Resolution] | Attempt [N]

## SECTION 1: PRODUCTION INTENT

[Pattern 1 template]

## SECTION 2: VALIDATED PATTERNS FROM PREVIOUS SUCCESSES

From Asset 1 (91/100): [Applicable patterns]
From Asset 2 (94/100): [Applicable patterns]

## SECTION 3: kr-motif INVENTORY WITH NEGATIVE CONSTRAINTS

PRIMARY (13-18cm):

- [kr-motif name]: [Description] | NOT [misinterpretation]

SECONDARY (8-12cm):

- [kr-motif name]: [Description] | NOT [misinterpretation]

TERTIARY (2-8cm):

- [kr-motif name]: [Description] | NOT [misinterpretation]

[DEPRECATED_STYLE] AUTHENTICITY TEST:
✓ [kr-motif]: Challenged European taxonomy via [reason]
✓ [kr-motif]: Australian endemic, [taxonomic significance]

## SECTION 4: TRANSLUCENCY PHYSICS (if applicable)

[Only if kr-motifs require transmission]

- [kr-motif]: [Tier X] [X-Y% light-transmissive]
  What visible through: [[DEPRECATED_STYLE] elements, background, other kr-motifs]

## SECTION 5: DENSITY ZONES WITH SAFETY MARGINS

Upper-left: 15% maximum | 200×200px COMPLETELY EMPTY
Lower-right: 25% maximum | 150×150px COMPLETELY EMPTY
Central: 60-80% [DEPRECATED_STYLE] [DEPRECATED_STYLE] composition

## SECTION 6: TYPOGRAPHY & DOCUMENTATION

Count: 5-6 labels maximum
Font: Crimson Text serif
Color: Cream --sys-color-paper-white at 85% opacity
Format: "Fig. 1. kr-leafus camaldulensis (River Red Gum)"

## SECTION 7: TECHNICAL OUTPUT

Background: #1A1714 (theatrical void, NO sepia drift)
Resolution: [WIDTHxHEIGHT]
Format: PNG maximum quality
[Special requirements: e.g., seamless tiling, edge-matching]

---

## CORRECTIONS FROM ATTEMPT [N-1] (if iteration):

SCORE: [X/100]

WHAT WORKED (preserve):
✓ [Success 1]
✓ [Success 2]

CRITICAL FIXES:
✗ [Violation 1] → FIX: [Specific correction language]
✗ [Violation 2] → FIX: [Specific correction language]

Automation Logic

Load asset specifications JSON
Query pattern library for applicable patterns
If asset_type == "pattern_tile" → add seamless tiling spec
Single Mode: Always apply "Solidarity" aesthetic overrides.
Map kr-motifs → taxonomic significance database
Generate negative constraints from common failures
If previous_attempt exists → prepend corrections section
Format as structured prompt
Output ready for Gemini paste

Token Optimization

Problem: Full prompt ~2000 tokens, Gemini Flash limit 1M tokens/min Solution: Template compression

Remove verbose explanations. Use shorthand:

DENSE (efficient):
"kr-leafus: 60% transmission, veins visible through leaf tissue"

VERBOSE (wasteful):
"The kr-leafus leaves should demonstrate approximately 60% light transmission, wherein the vein architecture becomes visible when light passes through the leaf tissue, creating an effect of [DEPRECATED_STYLE] translucency"

Target: 1200-1500 tokens per prompt (25-40% reduction)

Efficiency Gain

Before: 10 min manual prompt construction per attempt
After: 1 min automated generation
Savings: 9 min per attempt × 20-25 attempts = 3-4 hours saved

Error Prevention

Pattern inconsistency: ELIMINATED
Copy-paste errors: ELIMINATED
Missing negative constraints: ELIMINATED
Forgotten corrections: ELIMINATED
Token bloat: REDUCED 30%

Integration Workflow

Typical Usage Pattern

1. Request: "Use prompt-composer for Asset 3"
2. Claude generates optimized prompt
3. Paste into Gemini → generate
4. Request: "Use auto-validator on downloaded image"
5. Claude scores → decision: PACKAGE or REGENERATE
6. If PACKAGE: "Use asset-packager"
7. Complete bundle created in 3 min total

Integration Points

Flash-Sidekick MCP:

generate_idf - Extract design tokens from validated PNG
analyze_code_quality - Identify vague language in prompts
consult_pro - Validate kr-motif endemic status
quick_summarize - Generate narrative from kr-motif list

Gemini:

Auto-validator output → correction_prompt → paste directly into next generation
Prompt-composer output → copy-paste to Gemini AI Studio

Claude Code:

Delegates file operations and git commits
Verifies directory structure creation

Installation

Extract to Claude Desktop skills:

cd ~/.config/claude-desktop/skills/
cp -r /path/to/tier1-automation .

Agent Skills: Tier 1 Automation Skills

Install this agent skill to your local

Skill Files