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Embed reference media (photos, voice, video templates) as base64 data URIs in skills for self-sufficient, portable, consistent generation
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/fabioc-aloha/ChessCoach --skill visual-memoryコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SOC 職業分類に基づく
SKILL.md を表示中
| type | skill |
| lifecycle | stable |
| inheritance | inheritable |
| name | visual-memory |
| description | Embed reference media (photos, voice, video templates) as base64 data URIs in skills for self-sufficient, portable, consistent generation |
| tier | extended |
| applyTo | **/*visual*,**/*reference*,**/*portrait*,**/*base64* |
| muscle | .github/muscles/visual-memory.cjs |
| metadata | {"inheritance":"inheritable"} |
| currency | 2026-04-22T00:00:00.000Z |
Self-sufficient skills that carry their own reference media — no external folder dependencies.
Applies To: Any skill needing consistent visual identity, voice, or motion style across multiple generation tasks.
Skills that depend on external reference files (photo folders, audio samples) break when:
Embed optimized reference assets directly in the skill as base64 data URIs. The skill becomes fully self-sufficient — it works anywhere, exactly the same way, every time.
skill-folder/
├── SKILL.md
└── visual-memory/
├── index.json ← Metadata only (no binary data)
├── visual-memory.json ← Full base64 data URIs (~30-80KB per photo)
└── subject-1.jpg ← Optional: keep originals alongside
└── subject-2.jpg
Reference photos for face-consistent portrait generation. Embedded to eliminate folder dependencies.
| Spec | Value |
|---|---|
| Target size | 512px longest edge |
| Quality | 85% JPEG |
| Per-photo size | ~40-80KB (vs ~2MB originals) |
| Format | data:image/jpeg;base64,<encoded> |
| Quantity | 5-8 photos per subject, varied angles |
When to use: Face-consistent portrait generation, AI character references, persona avatars.
Moved to dedicated skill: See audio-memory/SKILL.md for voice sample storage and TTS cloning
# Install ImageMagick if needed:
# macOS: brew install imagemagick
# Windows: winget install ImageMagick.ImageMagick
# Resize single photo: 512px longest edge @ 85% JPEG quality
magick input.jpg -resize 512x512> -quality 85 output.jpg
# Batch resize folder
Get-ChildItem *.jpg | ForEach-Object {
magick $_.Name -resize "512x512>" -quality 85 "resized/$($_.Name)"
}
# Convert PNG to optimized JPG
magick input.png -resize "512x512>" -quality 85 output.jpg
import { readFileSync, writeFileSync } from "fs";
import { basename } from "path";
function toDataUri(imagePath) {
const buffer = readFileSync(imagePath);
const ext = imagePath.toLowerCase().endsWith(".png") ? "png" : "jpeg";
return `data:image/${ext};base64,${buffer.toString("base64")}`;
}
// Batch convert and write to JSON
const photos = ["photo1.jpg", "photo2.jpg", "photo3.jpg"];
const images = photos.map((p) => ({
filename: basename(p),
dataUri: toDataUri(p),
notes: "",
}));
writeFileSync("images.json", JSON.stringify({ images }, null, 2));
Quick PowerShell (single file to clipboard):
[Convert]::ToBase64String([IO.File]::ReadAllBytes("photo.jpg")) | Set-Clipboard
# Paste with prefix: "data:image/jpeg;base64,<paste>"
{
"schema": "visual-memory-v1",
"generated": "2026-03-01",
"subjects": {
"person-name": {
"description": "Brief visual description",
"ageInfo": {
"referenceAge": 30,
"birthYear": 1990,
"photoDate": "2026-03"
},
"images": [
{
"filename": "person-1.jpg",
"dataUri": "data:image/jpeg;base64,<base64-encoded-image>",
"notes": "Front-facing, natural lighting"
},
{
"filename": "person-2.jpg",
"dataUri": "data:image/jpeg;base64,<base64-encoded-image>",
"notes": "3/4 profile, outdoor"
}
]
}
}
}
{
"version": "1.0",
"generated": "2026-03-01",
"targetSize": 512,
"subjects": {
"person-name": {
"count": 7,
"files": ["person-1.jpg", "person-2.jpg", "person-3.jpg"]
}
}
}
import { readFileSync } from "fs";
import { join, dirname } from "path";
import { fileURLToPath } from "url";
const __dirname = dirname(fileURLToPath(import.meta.url));
const VISUAL_MEMORY_PATH = join(
__dirname,
".github/skills/<skill-name>/visual-memory/visual-memory.json"
);
function loadVisualMemory() {
const data = JSON.parse(readFileSync(VISUAL_MEMORY_PATH, "utf8"));
return Object.fromEntries(
Object.entries(data.subjects).map(([name, subject]) => [
name,
subject.images.map((i) => i.dataUri),
])
);
}
const visualMemory = loadVisualMemory();
// visualMemory.personName → array of data URIs
The reference photos speak for themselves. Only describe:
NEVER include:
| Model | Parameter | Max Refs | Notes |
|---|---|---|---|
nano-banana-pro | image_input | 14 | Array of data URIs, 4K output |
nano-banana-2 | image_input | 14 | Faster/cheaper alternative (Gemini 3.1 Flash) |
flux-2-pro | input_images | 8 | Array of data URIs |
flux-2-flex | input_images | 10 | Max-quality editing |
ideogram-v2 | ❌ None | — | No face reference |
Always start the prompt with explicit reference instruction:
Generate a photo of EXACTLY the person shown in the reference images.
For multiple subjects at once:
Generate a photo with two people.
LEFT: EXACTLY the person from [Name A]'s reference images, wearing [clothing].
RIGHT: EXACTLY the person from [Name B]'s reference images, wearing [clothing].
[Scene description]. [Lighting]. Professional photography.
Store consistent motion style as JSON — not actual video files:
{
"videoStyles": {
| ---------- | ------------------------------------------- |
| Quantity | 5-8 photos (more = better likeness) |
| Angles | Front, 3/4 left, 3/4 right, slight profile |
| Lighting | Mixed (natural, indoor, flash, outdoor) |
| Expression | Neutral, smiling, serious — varied |
| File size | 40-80KB each after 512px/85% optimization |
| Total size | ~500KB for 8-10 photos — acceptable |
> **For voice samples**: See [audio-memory/SKILL.md](../audio-memory/SKILL.md)
---
## Image Embed Commit Gate (FC4)
Before committing a new generated image to visual memory (`visual-memory.json`, `portraits/`, or `assets/`), the LLM reviews the generation report (from GP1's `generation-report.mjs`) against these criteria. No image enters canonical memory without passing this gate.
| # | Check | Pass | Fail | Action on Fail |
|---|-------|------|------|----------------|
| 1 | **Subject identity match** — generated face matches reference image | Same person, recognizable features | Wrong face, different age/ethnicity, identity drift | Regenerate with stronger identity section; increase reference weight |
| 2 | **Prompt fidelity** — scene, pose, expression match prompt | All major elements present | Missing key element (wrong setting, wrong expression) | Revise prompt; check model supports requested style |
| 3 | **AI artifact scan** — no visible generation artifacts | Clean render, natural anatomy | Extra fingers, melted features, seam lines, floating objects | Regenerate; reduce prompt complexity; try different model |
| 4 | **Text legibility** (if applicable) — overlay text is correct and readable | Text spelled correctly, fully visible | Garbled, misspelled, or cut off | Use ideogram model; simplify text; reduce word count |
| 5 | **Brand alignment** — colors, style match project identity | Consistent with brand guide | Off-brand palette, wrong visual style | Add explicit hex codes to prompt; reference brand-asset-management |
| 6 | **Resolution match** — output dimensions fit target use case | Correct aspect ratio and pixel dimensions | Wrong ratio (portrait instead of banner, too small for print) | Specify exact dimensions in generation parameters |
| 7 | **Encoding size** — base64 embedding stays within token budget | ≤256px longest edge for embedded dataUri (~13KB) | Full resolution in dataUri (42KB+, context overflow risk) | Resize to 256px before encoding; keep original at full resolution |
| 8 | **Duplicate check** — asset doesn't duplicate existing entry | New asset or intentional replacement | Near-identical image already exists in visual memory | Skip commit; use existing asset; or mark as variant |
| 9 | **Metadata completeness** — entry has required fields | id, model, prompt, outputPath, qaStatus=ok | Missing fields or qaStatus still pending | Complete generation report entry before committing |
| 10 | **Storage location** — file goes to correct directory | Portraits → `portraits/`, banners → `images/`, brand → `assets/` | File placed in wrong directory | Move to correct location per asset type conventions |
**Workflow**: Generate → review `.generation-report.json` → pass all rows above → commit to visual memory. Images that fail any row stay in staging (output directory) until fixed or discarded.
## Benefits Summary
| Without Visual Memory | With Visual Memory |
| ------------------------- | ------------------------------------- |
| External photo folder required | No external dependencies |
| Breaks on different machines | Works anywhere |
| Manual path management | Always correct path |
| Version control nightmare | JSON in version control |
| Different results per machine | Exact consistency everywhere |
| ~2MB unoptimized originals | ~50MB → 500KB optimized |
> **For voice samples**: See [audio-memory/SKILL.md](../audio-memory/SKILL.md)
## Visual/Audio Asset Staging Gate (GP3)
New generated assets (images, audio, video) must land in a **staging area** before promotion to canonical memory. This mirrors the AI-Memory promotion boundary (SK2) — no silent writes to permanent stores.
### Staging Flow
```text
Generate → staging/ → review (FC4 table + this gate) → promote to canonical memory
↘ reject → delete or archive in staging/rejected/
| # | Check | Pass | Fail | Action on Fail |
|---|---|---|---|---|
| 1 | Staging directory used — generated asset lands in staging/ or output dir, not directly in canonical memory | Asset path is in staging area | Asset written directly to portraits/, assets/, or memory JSON | Move to staging; update generation script to use staging path |
| 2 | Generation report exists — .generation-report.json accompanies the asset | Report file present with model, prompt, parameters, timestamp | Asset exists without provenance metadata | Generate report retroactively or regenerate with report enabled |
| 3 | FC4 commit gate passed — all 10 rows of Image Embed Commit Gate evaluated | All FC4 rows pass or have documented exceptions | FC4 not run or rows failing without justification | Run FC4 review; fix or document exceptions before promoting |
| 4 | No duplicate in canonical — asset doesn't duplicate an existing canonical asset | No visually/audibly similar asset in target memory location | Duplicate or near-duplicate already exists | Keep better version; archive or delete the other |
| 5 | Operator/LLM confirmation — promotion requires explicit approval | Operator or LLM reviews staging asset and approves | Auto-promoted without review | Add confirmation step to generation pipeline |
| 6 | Decision logged — promotion decision recorded in PE1 decision log | logPhase2Decision() called with accept/reject + rationale | Asset promoted or deleted with no audit trail | Log decision before moving file |
| 7 | Backup of replaced asset — if promoting over an existing canonical asset, original is backed up | .backup copy of original created before overwrite | Original silently overwritten | Create backup; include path in decision log entry |
| Asset Type | Staging Directory | Canonical Directory |
|---|---|---|
| Portraits | images/staging/portraits/ | images/portraits/ or portrait JSON dataUri |
| Banners | images/staging/banners/ | assets/ or README inline |
| Blog images | images/staging/blog/ | master-wiki/blog/assets/ |
| Audio samples | audio/staging/ | Audio memory JSON dataUri |
| Video clips | video/staging/ | Video memory or assets directory |
Note: Generation scripts (generate-*.js) should default to writing into images/staging/ rather than the final destination. The promotion step is a separate manual or LLM-assisted action.