Agent Skills: Prompt-Composer Skill

"Automated prompt generation from pattern library. Input asset specifications\

UncategorizedID: okgoogle13/careercopilot/prompt-composer

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pnpm dlx add-skill https://github.com/okgoogle13/careercopilot/tree/HEAD/.claude/skills/prompt-composer

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.claude/skills/prompt-composer/SKILL.md

Skill Metadata

Name
prompt-composer
Description
"Automated prompt generation from pattern library. Input asset specifications\

Prompt-Composer Skill

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": "Gallery",
  "specimens": ["Eucalyptus", "Wattle", "Banksia", "Gum nuts"],
  "primary_focus": "Botanical only (no fauna)",
  "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: Geographic Authenticity

ENDEMIC TEST: Every specimen must answer "Did this organism challenge European taxonomy?"
AUSTRALIAN ONLY: NO European fauna, generic specimens, northern hemisphere species
MANDATORY CHECK: [List specimens] → validate against Australian Flora/Fauna Database

Pattern 3: Translucency Tiers

TIER 1 (60-80% transmission): Spider molt, thin membranes
TIER 2 (40-60% transmission): Frill-neck 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 specimen: "This is NOT [misinterpretation]"
Echidna: NOT full animal body, NOT cute portrait
Spider: NOT live crawling, NOT opaque solid
Frill-neck: 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% Wunderkammer 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: SPECIMEN INVENTORY WITH NEGATIVE CONSTRAINTS

PRIMARY (13-18cm):

- [Specimen name]: [Description] | NOT [misinterpretation]

SECONDARY (8-12cm):

- [Specimen name]: [Description] | NOT [misinterpretation]

TERTIARY (2-8cm):

- [Specimen name]: [Description] | NOT [misinterpretation]

GEOGRAPHIC AUTHENTICITY TEST:
✓ [Specimen]: Challenged European taxonomy via [reason]
✓ [Specimen]: Australian endemic, [taxonomic significance]

## SECTION 4: TRANSLUCENCY PHYSICS (if applicable)

[Only if specimens require transmission]

- [Specimen]: [Tier X] [X-Y% light-transmissive]
  What visible through: [botanical elements, background, other specimens]

## 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% Wunderkammer organic 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. Eucalyptus 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

  1. Load asset specifications JSON
  2. Query pattern library for applicable patterns
  3. If asset_type == "pattern_tile" → add seamless tiling spec
  4. If mode == "Laboratory" → apply clinical aesthetic overrides
  5. Map specimens → taxonomic significance database
  6. Generate negative constraints from common failures
  7. If previous_attempt exists → prepend corrections section
  8. Format as structured prompt
  9. Output ready for Gemini paste

Integration Points

Flash-Sidekick:

  • Call consult_pro with specimen list → validate endemic status
  • Call analyze_code_quality on draft prompt → identify vague language

Auto-Validator:

  • Takes previous attempt's correction_prompt field
  • Injects into next generation's CORRECTIONS section

Pattern-Learner:

  • Queries pattern library for latest validated patterns
  • Applies success learnings from recent high-scoring assets

Token Optimization

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

Remove verbose explanations. Use shorthand:

DENSE (efficient):
"Eucalyptus: 60% transmission, veins visible through leaf tissue"

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

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

Usage Example

prompt = prompt_composer.generate(
    asset_id="ASSET-3",
    asset_name="Nocturnal Canopy Pattern",
    specs={
        "resolution": "512x512",
        "specimens": ["Eucalyptus", "Wattle", "Banksia"],
        "special": ["seamless_tile"]
    },
    previous_attempt={
        "score": 85,
        "violations": ["edge_mismatch", "density_25%"]
    }
)

# Copy-paste to Gemini AI Studio → generate

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%

Transforms pattern library into executable templates. Manual prompt crafting → automated compilation.