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Photo Booth

Organization
pnp
photo-booth

Create a photorealistic 3x3 reference-locked couple photo-booth grid with synchronized expressions, poses, and strict visual continuity.

Overview

Publisherpnp
Repositorycopilot-prompts
Skill namephoto-booth
Stars
894
Forks
307
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by pnp on GitHub. Read the source before you install it.

Installation

Install the Photo Booth AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pnp/copilot-prompts/tree/main/samples/skills/photo-booth
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pnp/copilot-prompts.git /tmp/copilot-prompts
mkdir -p .claude/skills
cp -r /tmp/copilot-prompts/samples/skills/photo-booth .claude/skills/photo-booth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Photo Booth in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Photo Booth on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Photo Booth is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

photo-booth

When to use this skill

  • Use when the user wants a photo-booth grid, emotion grid, couple selfie grid, synchronized pose sheet, or multiple portrait variations from reference images.
  • Trigger on requests for a 3x3 grid with the same subjects, room, wardrobe, framing, and lighting while expressions or micro-poses change.

Required inputs

  • A primary reference image for the main person.
  • An optional reference image for the partner or character.
  • The desired partner concept if no partner reference is supplied.
  • Any requested wardrobe, backdrop, photographic era, pose list, or mood.

If the primary reference image is missing, ask the user to attach it. Ask one focused question when the partner concept or intended style is unclear.

Continuity rules

  • Preserve the primary person's visible facial structure, skin tone, distinguishing features, eye color, hair color, and other reference-grounded traits.
  • Do not identify the real person or infer private attributes.
  • Keep both subjects consistent in all nine panels.
  • Lock the environment, curtain or backdrop, lighting direction and softness, camera distance, framing, wardrobe, accessories, material textures, and color treatment.
  • Allow only facial expressions, small head movements, and couple micro-poses to change.
  • Require both subjects to perform the same or mirrored gesture in each panel. For paired actions such as an embrace, require balanced and symmetrical blocking.
  • Keep both faces visible. Prevent either subject, hands, clothing, or props from blocking the primary person's face.
  • Do not add logos, captions, watermarks, or unrequested objects.

Default visual treatment

  • Photorealistic close-up couple selfie portrait.
  • Both subjects shoulder-to-shoulder at the same head height and distance from the camera.
  • Soft, bright frontal window light with a mild on-camera flash feel.
  • Subtle analog film grain, candid realism, natural skin texture, visible pores, and minimal retouching.
  • Tasteful, fully opaque wardrobe with realistic fabric texture.
  • Thin white separators between panels.
  • Square overall output with nine square cells.

Adapt these defaults to the user's reference and request. Do not force a photographic era, costume, gender presentation, or wardrobe that the user did not request.

Workflow

1. Analyze the references

  • Describe each visible subject and the visual traits that must remain stable.
  • Identify the backdrop, lighting, camera distance, framing, wardrobe, accessories, palette, and texture.
  • If a partner reference is absent, use the requested fictional or generic partner concept without claiming brand ownership or a real identity.

2. Define the continuity lock

Create a compact list covering:

  • Identity and appearance anchors.
  • Wardrobe and accessory anchors.
  • Partner design anchors.
  • Room and backdrop anchors.
  • Lighting, camera, lens, grain, and color anchors.
  • Face visibility and pose synchronization constraints.

3. Plan nine synchronized panels

Use the user's pose list when supplied. Otherwise use this balanced default progression:

  1. Soft smile with mirrored peace signs.
  2. Bright smile with symmetrical cheek-to-cheek lean.
  3. Warm balanced embrace with both faces visible.
  4. Candid laugh with the same lean-in angle.
  5. Synchronized blown kiss.
  6. Tasteful symmetrical affectionate pose requested by the user.
  7. Matching playful expression and hand gesture.
  8. Calm editorial stance with mirrored arm position.
  9. Confident power-couple pose with equivalent silhouette gestures.

For every panel specify:

  • Expression and emotion.
  • Synchronized or mirrored gesture.
  • Head angle and eye direction.
  • Face visibility constraints.
  • Any allowed micro-variation from the locked composition.

4. Generate the grid

  • Always call image_generation.
  • Request one ultra-detailed 3x3 master image containing all nine panels.
  • Provide detailed generation instructions that include the reference lock, partner consistency, environment, lighting, camera distance, wardrobe, texture, synchronized pose rule, panel sequence, separators, square layout, and no-text requirement.
  • Require consistent photographic realism and natural sharpness across the entire grid.

5. Review before responding

Confirm:

  • The same subjects appear in all nine panels.
  • Identity, wardrobe, backdrop, lighting, framing, and camera distance remain stable.
  • Each panel follows its assigned expression and synchronized pose.
  • Faces are recognizable and unobstructed.
  • No extra characters, limbs, objects, logos, labels, or text were introduced.

If the generated grid violates continuity, revise the generation instructions and retry once. If the retry fails, explain the specific continuity issue and ask whether the user wants another attempt.

Output

  • Present the single generated 3x3 grid.
  • Briefly summarize the locked style and subjects.
  • List the nine panel poses in order so the user can request a targeted regeneration.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Photo Booth AI skill do?

Create a photorealistic 3x3 reference-locked couple photo-booth grid with synchronized expressions, poses, and strict visual continuity.

Why use Photo Booth on TypingMind?

Because you install it once and use it with any model. Photo Booth is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Photo Booth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pnp/copilot-prompts/tree/main/samples/skills/photo-booth. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Photo Booth?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Photo Booth?

As many as you like. As long as a model supports skills, you can use Photo Booth with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Photo Booth AI skill free?

Yes. It is published on GitHub by pnp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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