القائمة
Embed reference media (photos, voice, video templates) as base64 data URIs in skills for self-sufficient, portable, consistent generation
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/fabioc-aloha/ChessCoach --skill visual-memoryيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
Detect, create, and manage the AI-Memory fleet communication channel. Fires on bootstrap, session start (announcements), and feedback writes.
Generate on-brand Alex — ACT Edition SVG banners for documents (READMEs, plans, notes, release artifacts)
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| 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.