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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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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.
The Problem
Skills that depend on external reference files (photo folders, audio samples) break when:
Skill is synced to a new machine without the original files
Files are renamed, moved, or deleted
A different project inherits the skill
Version control doesn't track binary assets
The Solution: Visual Memory
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
Memory Types
Visual Memory (Photos as Base64)
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.
Audio Memory
Moved to dedicated skill: See audio-memory/SKILL.md for voice sample storage and TTS cloning
Do NOT Describe Physical Appearance When Using References
The reference photos speak for themselves. Only describe:
Scene / setting
Clothing (specific colors, styles)
Expression (smile, serious, thoughtful)
Lighting (natural, studio, dramatic)
Background (office, outdoors, neutral)
Action / pose
NEVER include:
Hair color, style, or texture
Eye color
Skin tone or complexion
Body type / build
Any physical description of the person
Model API Parameters for Reference Images
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
Prompt Anchor Pattern
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.
Video Memory (Style Templates)
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/
Staging Gate Decision Table
#
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
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
Staging Directories by Asset Type
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.
