alex-character

name	alex-character
description	Alex Finch visual identity — reference portraits with embedded base64 data URIs for face-consistent AI image generation
tier	extended
applyTo	**/generate-alex*,**/*alex*portrait*,**/*alex*avatar*,**/*character*
muscle	null
metadata	{"inheritance":"master-only"}

Alex Character Visual Memory

Self-sufficient visual identity for Alex Finch with embedded reference portraits across all life stages.

Applies To: Any image generation task requiring Alex's consistent visual appearance.

---

Alex Finch — Visual Identity

Attribute	Value
Name	Alex Finch
Reference Age	26 years old
Hair	Curly ginger copper-red
Eyes	Striking blue-green
Skin	Fair with light freckles across nose and cheeks
Expression	Intelligent, curious

Immutable Traits

These traits MUST be preserved in all generated images:

1. Curly ginger copper-red hair — distinctive, consistent across all ages
2. Striking blue-green eyes — clear, expressive
3. Fair skin with light freckles — across nose and cheeks
4. Intelligent curious expression — engaged, thoughtful

---

Visual Memory Contents

Source of Truth

The reference portrait is the canonical image — all generation must maintain visual consistency with this.

File	Age	Disk Size	Base64 (256px)	Origin
alex-reference.jpg	16	31 KB	13 KB	Human-curated

Generated Artifacts

Age progressions are derived from the source of truth. They can be regenerated if the model or prompts improve.

Currently empty — regenerate with scripts/generate-alex-age-progression.js when needed.

Model: nano-banana-pro via Replicate

---

Using Visual Memory

Loading References

import { readFileSync } from 'fs';
import { join } from 'path';

const SKILL_PATH = '.github/skills/alex-character/visual-memory';

function loadAlexVisualMemory() {
  const data = JSON.parse(readFileSync(
    join(SKILL_PATH, 'visual-memory.json'), 
    'utf8'
  ));
  return data.subjects.alex;
}

// Get reference for specific age
function getAlexReferenceByAge(targetAge) {
  const alex = loadAlexVisualMemory();
  const closest = alex.images.reduce((prev, curr) => 
    Math.abs(curr.age - targetAge) < Math.abs(prev.age - targetAge) ? curr : prev
  );
  return closest.dataUri;
}

With nano-banana-pro (Face-Consistent)

import Replicate from 'replicate';

const replicate = new Replicate();

async function generateAlexPortrait(age, scene, expression) {
  const reference = getAlexReferenceByAge(age);
  
  const output = await replicate.run("google/nano-banana-pro", {
    input: {
      image: reference,  // data URI works directly
      prompt: `
        Age ${age} portrait.
        Scene: ${scene}
        Expression: ${expression}
        Style: photorealistic, natural lighting, professional quality
      `.trim(),
      aspect_ratio: "1:1",
      output_format: "png",
      safety_filter_level: "block_only_high"
    }
  });
  
  return output;
}

---

Generation Rules

DO

• ✅ Use the closest age reference for the target age
• ✅ Describe scene, clothing, expression, and lighting
• ✅ Specify camera/lens style for consistent quality
• ✅ Use photorealistic style for consistency

DO NOT

• ❌ Describe physical appearance (hair, eyes, skin) — the reference handles this
• ❌ Mix references from different ages in the same generation
• ❌ Request changes to immutable traits
• ❌ Use non-face-consistent models without reference

---

Prompt Templates

Standard Portrait

Age {age} portrait.
Scene: {setting}
Expression: {expression}
Attire: {clothing}
Style: photorealistic portrait, natural lighting, professional quality, shallow depth of field, 85mm lens
One person only. No text overlays.

Professional Scene

{age}-year-old professional in {setting}.
Expression: {expression}
Wearing: {clothing}
Style: editorial photography, soft studio lighting, clean background
Single subject only.

Age-Specific Expressions

Age Range	Recommended Expression
3-7	Pure wonder, bright curious eyes
13-15	Thoughtful, curious, ready to learn
18-25	Confident, ambitious, focused
26-35	Composed, expert, approachable
42-55	Wise, mentoring, experienced
62+	Serene, knowing, graceful

---

File Structure

.github/skills/alex-character/
├── SKILL.md                     ← This file
└── visual-memory/
    ├── index.json               ← Metadata (targetSize, b64EmbedSize)
    ├── visual-memory.json       ← Base64 embeddings (resized to 256px)
    ├── alex-reference.jpg       ← Primary reference (512px, 31 KB)
    └── alex-age-*.jpg           ← Age progressions (when generated)

---

Base64 Embedding Rules

Problem: Full-resolution base64 images quickly exceed context window limits.

Solution: Images embedded in visual-memory.json are resized to 256px (longest edge) before base64 encoding.

Metric	512px	256px	Savings
Base64 size	~42 KB	~13 KB	70%
13 images	~550 KB	~170 KB	70%

Embedding Workflow

1. Original files remain at targetSize (512px) in the visual-memory folder
2. When embedding to base64: resize to b64EmbedSize (256px)
3. Face-consistent models (nano-banana-pro) work fine with 256px references
4. Document the resize in the embedSize field of each entry

PowerShell Resize + Encode

Add-Type -AssemblyName System.Drawing
$img = [System.Drawing.Image]::FromFile($srcPath)
$targetSize = 256
# ... resize maintaining aspect ratio ...
$bytes = [System.IO.File]::ReadAllBytes($resizedPath)
$base64 = [Convert]::ToBase64String($bytes)
$dataUri = "data:image/jpeg;base64,$base64"

---

Related Skills

• visual-memory — General visual memory pattern
• character-aging-progression — Age-specific generation rules
• ai-character-reference-generation — Reference photo generation
• image-handling — Format selection and Replicate models

---

Version History

Version	Date	Changes
1.0	April 2026	Initial visual memory with 14 embedded references