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muapi-photo-pack-generator

samuraigpt/generative-media-skills

How to install muapi-photo-pack-generator

npx skills add https://github.com/samuraigpt/generative-media-skills --skill muapi-photo-pack-generator
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Full instructions (SKILL.md)

Source of truth, from samuraigpt/generative-media-skills.


name: muapi-photo-pack-generator version: 0.1.0 description: Generate a pack of professional or aesthetic photos from a single reference image while preserving the exact identity of the person.

๐Ÿ“ธ Photo Pack Generator Expert Skill (Identity-Lock Edition)

Transform a single reference photo into a collection of themed images while maintaining extremely high facial identity fidelity.

This skill prioritizes identity preservation first, then applies stylistic transformations like LinkedIn portraits, dating photos, cinematic shots, or fantasy styles.

The system uses Identity Lock Prompting instead of describing the person, preventing the model from generating a new face.


Core Principles

1๏ธโƒฃ Identity Lock (MOST IMPORTANT)

The generated images must always depict the same person from the reference image.

All prompts MUST include identity lock instructions.

Required identity rules:

  • Preserve the exact facial identity from the reference image
  • Do not modify eye shape or spacing
  • Do not modify nose structure
  • Do not modify jawline or chin shape
  • Do not modify cheekbones
  • Do not modify face proportions
  • Identity must remain identical to the reference photo

2๏ธโƒฃ Vision-First Scene Analysis

The agent MUST analyze the reference image before generation.

However the analysis must NOT describe the person (age, ethnicity, hair etc).

Allowed analysis fields:

  • head orientation
  • facial angle
  • expression
  • lighting direction
  • framing (portrait / half body / full body)

Example:

Head orientation: slight left tilt
Expression: neutral friendly
Lighting: soft frontal light
Framing: head and shoulders portrait


Agent Execution Flow

Step 1 โ€” Grounding Check

Ensure the user has provided a reference image.

Supported inputs:

  • local image
  • URL
  • uploaded file

Step 2 โ€” Vision Analysis

Extract scene attributes only.

DO NOT describe:

  • age
  • ethnicity
  • beard
  • hair
  • body type

Identity must come directly from the image.


Step 3 โ€” Category Selection

If the user does not specify a category suggest:

  • LinkedIn
  • Tinder
  • OldMoney

Step 4 โ€” Prompt Construction

Use the reference image as the identity source.

Preserve the exact facial identity from the reference image.

Identity must remain identical to the reference photo.

Do not change:

  • eye shape
  • eye spacing
  • nose structure
  • jawline
  • cheekbones
  • face proportions

Maintain similar head orientation as the reference.

Scene example:

Outdoor cafรฉ portrait
Soft natural daylight
35mm portrait lens
Shallow depth of field
Photorealistic skin texture


Step 5 โ€” Negative Prompt

Always include:

different person
altered face
changed facial features
new identity
generic face
beautified face
plastic skin
face distortion


Step 6 โ€” Execution

Example:

bash scripts/generate-pack.sh
--image "./my_face.jpg"
--category "LinkedIn"
--identity-lock true
--num 5


Supported Categories

CategoryBest ForAesthetic
LinkedInProfessionalStudio
CEOFoundersOffice
TinderDatingLifestyle
OldMoneyLuxuryEstate
CyberpunkFantasyNeon
FitnessGymAthletic
TravelSocialBali/Paris
90sRetroVintage
HolidaySeasonalFestive

Guardrails

Fidelity First

Identity preservation is always more important than style.

Never Re-Describe the Person

Avoid prompts like:

"Indian man in his 20s with short hair"

This causes the model to generate a new face.

Identity must come from the reference image only.


Recommended Models

Best results with:

  • nano-banana-edit

Result

This system produces:

  • consistent identity
  • photorealistic images
  • multi-style photo packs
  • professional outputs

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