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Vgl

Organization
Bria-AI
vgl

Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria Fibo models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.

Overview

PublisherBria-AI
Repositorybria-skill
Skill namevgl
Stars
74
Forks
7
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Bria-AI on GitHub. Read the source before you install it.

Installation

Install the Vgl 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/Bria-AI/bria-skill/tree/dev/bria-ai-openclaw/skills/vgl
  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/Bria-AI/bria-skill.git /tmp/bria-skill
mkdir -p .claude/skills
cp -r /tmp/bria-skill/bria-ai-openclaw/skills/vgl .claude/skills/vgl
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vgl 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 Vgl 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 Vgl 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.

Bria VGL — Full Control Over Image Generation

Define every visual attribute as structured JSON instead of hoping natural language gets it right. VGL (Visual Generation Language) gives you explicit, deterministic control over objects, lighting, camera settings, composition, and style for Bria's Fibo models.

Related Skill: Use bria-ai to execute these VGL prompts via the Bria API. VGL defines the structured control format; bria-ai handles generation, editing, and background removal.

Core Concept

VGL replaces ambiguous natural language prompts with deterministic JSON that explicitly declares every visual attribute: objects, lighting, camera settings, composition, and style. This ensures reproducible, controllable image generation.

Operation Modes

ModeInputOutputUse Case
GenerateText promptVGL JSONCreate new image from description
EditImage + instructionVGL JSONModify reference image
Edit_with_MaskMasked image + instructionVGL JSONFill grey masked regions
CaptionImage onlyVGL JSONDescribe existing image
RefineExisting JSON + editUpdated VGL JSONModify existing prompt

JSON Schema

Output a single valid JSON object with these required keys:

1. short_description (String)

Concise summary of image content, max 200 words. Include key subjects, actions, setting, and mood.

2. objects (Array, max 5 items)

Each object requires:

json
{
  "description": "Detailed description, max 100 words",
  "location": "center | top-left | bottom-right foreground | etc.",
  "relative_size": "small | medium | large within frame",
  "shape_and_color": "Basic shape and dominant color",
  "texture": "smooth | rough | metallic | furry | fabric | etc.",
  "appearance_details": "Notable visual details",
  "relationship": "Relationship to other objects",
  "orientation": "upright | tilted 45 degrees | facing left | horizontal | etc."
}

Human subjects add:

json
{
  "pose": "Body position description",
  "expression": "winking | joyful | serious | surprised | calm",
  "clothing": "Attire description",
  "action": "What the person is doing",
  "gender": "Gender description",
  "skin_tone_and_texture": "Skin appearance"
}

Object clusters add:

json
{
  "number_of_objects": 3
}

Size guidance: If a person is the main subject, use "medium-to-large" or "large within frame".

3. background_setting (String)

Overall environment, setting, and background elements not in objects.

4. lighting (Object)

json
{
  "conditions": "bright daylight | dim indoor | studio lighting | golden hour | blue hour | overcast",
  "direction": "front-lit | backlit | side-lit from left | top-down",
  "shadows": "long, soft shadows | sharp, defined shadows | minimal shadows"
}

5. aesthetics (Object)

json
{
  "composition": "rule of thirds | symmetrical | centered | leading lines | medium shot | close-up",
  "color_scheme": "monochromatic blue | warm complementary | high contrast | pastel",
  "mood_atmosphere": "serene | energetic | mysterious | joyful | dramatic | peaceful"
}

For people as main subject, specify shot type in composition: "medium shot", "close-up", "portrait composition".

6. photographic_characteristics (Object)

json
{
  "depth_of_field": "shallow | deep | bokeh background",
  "focus": "sharp focus on subject | soft focus | motion blur",
  "camera_angle": "eye-level | low angle | high angle | dutch angle | bird's-eye",
  "lens_focal_length": "wide-angle | 50mm standard | 85mm portrait | telephoto | macro"
}

For people: Prefer "standard lens (35mm-50mm)" or "portrait lens (50mm-85mm)". Avoid wide-angle unless specified.

7. style_medium (String)

"photograph" | "oil painting" | "watercolor" | "3D render" | "digital illustration" | "pencil sketch"

Default to "photograph" unless explicitly requested otherwise.

8. artistic_style (String)

If not photograph, describe characteristics in max 3 words: "impressionistic, vibrant, textured"

For photographs, use "realistic" or similar.

9. context (String)

Describe the image type/purpose:

  • "High-fashion editorial photograph for magazine spread"
  • "Concept art for fantasy video game"
  • "Commercial product photography for e-commerce"

10. text_render (Array)

Default: empty array []

Only populate if user explicitly provides exact text content:

json
{
  "text": "Exact text from user (never placeholder)",
  "location": "center | top-left | bottom",
  "size": "small | medium | large",
  "color": "white | red | blue",
  "font": "serif typeface | sans-serif | handwritten | bold impact",
  "appearance_details": "Metallic finish | 3D effect | etc."
}

Exception: Universal text integral to objects (e.g., "STOP" on stop sign).

11. edit_instruction (String)

Single imperative command describing the edit/generation.

Edit Instruction Formats

For Standard Edits (no mask)

Start with action verb, describe changes, never reference "original image":

CategoryRewritten Instruction
Style changeTurn the image into the cartoon style.
Object attributeChange the dog's color to black and white.
Add elementAdd a wide-brimmed felt hat to the subject.
Remove objectRemove the book from the subject's hands.
Replace objectChange the rose to a bright yellow sunflower.
LightingChange the lighting from dark and moody to bright and vibrant.
CompositionChange the perspective to a wider shot.
Text changeChange the text "Happy Anniversary" to "Hello".
QualityRefine the image to obtain increased clarity and sharpness.

For Masked Region Edits

Reference "masked regions" or "masked area" as target:

IntentRewritten Instruction
Object generationGenerate a white rose with a blue center in the masked region.
ExtensionExtend the image into the masked region to create a scene featuring...
Background fillCreate the following background in the masked region: A vast ocean extending to horizon.
Atmospheric fillFill the background masked area with a clear, bright blue sky with wispy clouds.
Subject restorationRestore the area in the mask with a young woman.
Environment infillCreate inside the masked area: a greenhouse with rows of plants under glass ceiling.

Fidelity Rules

Standard Edit Mode

Preserve ALL visual properties unless explicitly changed by instruction:

  • Subject identity, pose, appearance
  • Object existence, location, size, orientation
  • Composition, camera angle, lens characteristics
  • Style/medium

Only change what the edit strictly requires.

Masked Edit Mode

  • Preserve all visible (non-masked) portions exactly
  • Fill grey masked regions to blend seamlessly with unmasked areas
  • Match existing style, lighting, and subject matter
  • Never describe grey masks—describe content that fills them

Example Output

json
{
  "short_description": "A professional businesswoman in a navy blazer stands confidently in a modern glass office, holding a tablet. Natural daylight streams through floor-to-ceiling windows, creating a warm, productive atmosphere.",
  "objects": [
    {
      "description": "A confident businesswoman in her 30s with shoulder-length dark hair, wearing a tailored navy blazer over a white blouse. She holds a tablet in her left hand while gesturing naturally with her right.",
      "location": "center-right",
      "relative_size": "large within frame",
      "shape_and_color": "Human figure, navy and white clothing",
      "texture": "smooth fabric, professional attire",
      "appearance_details": "Minimal jewelry, well-groomed professional appearance",
      "relationship": "Main subject, interacting with tablet",
      "orientation": "facing slightly left, three-quarter view",
      "pose": "Standing upright, relaxed professional stance",
      "expression": "confident, approachable smile",
      "clothing": "Tailored navy blazer, white silk blouse, dark trousers",
      "action": "Presenting or reviewing information on tablet",
      "gender": "female",
      "skin_tone_and_texture": "Medium warm skin tone, healthy smooth complexion"
    },
    {
      "description": "A modern tablet device with a bright display showing charts and graphs",
      "location": "center, held by subject",
      "relative_size": "small",
      "shape_and_color": "Rectangular, silver frame with illuminated screen",
      "texture": "smooth glass and metal",
      "appearance_details": "Thin profile, business application visible on screen",
      "relationship": "Held by businesswoman, focus of her attention",
      "orientation": "vertical, screen facing viewer at slight angle",
      "pose": null,
      "expression": null,
      "clothing": null,
      "action": null,
      "gender": null,
      "skin_tone_and_texture": null,
      "number_of_objects": null
    }
  ],
  "background_setting": "Modern corporate office interior with floor-to-ceiling windows overlooking a city skyline. Minimalist furniture in neutral tones, potted plants adding touches of green.",
  "lighting": {
    "conditions": "bright natural daylight",
    "direction": "side-lit from left through windows",
    "shadows": "soft, natural shadows"
  },
  "aesthetics": {
    "composition": "rule of thirds, medium shot",
    "color_scheme": "professional blues and neutral whites with warm accents",
    "mood_atmosphere": "confident, professional, welcoming"
  },
  "photographic_characteristics": {
    "depth_of_field": "shallow, background slightly soft",
    "focus": "sharp focus on subject's face and upper body",
    "camera_angle": "eye-level",
    "lens_focal_length": "portrait lens (85mm)"
  },
  "style_medium": "photograph",
  "artistic_style": "realistic",
  "context": "Corporate portrait photography for company website or LinkedIn professional profile.",
  "text_render": [],
  "edit_instruction": "Generate a professional businesswoman in a modern office environment holding a tablet."
}

Common Pitfalls

  1. Don't invent text - Keep text_render empty unless user provides exact text
  2. Don't over-describe - Max 5 objects, prioritize most important
  3. Match the mode - Use correct edit_instruction format for masked vs standard edits
  4. Preserve fidelity - Only change what's explicitly requested
  5. Be specific - Use concrete values ("85mm portrait lens") not vague terms ("nice camera")
  6. Null for irrelevant - Human-specific fields should be null for non-human objects

curl Example

bash
curl -X POST "https://engine.prod.bria-api.com/v2/image/generate" \
  -H "api_token: $BRIA_API_KEY" \
  -H "Content-Type: application/json" \
  -H "User-Agent: BriaSkills/1.3.7" \
  -d '{
    "structured_prompt": "{\"short_description\": \"...\", ...}",
    "prompt": "Generate this scene",
    "aspect_ratio": "16:9"
  }'

References

  • Schema Reference - Complete JSON schema with all parameter values
  • bria-ai - API client and endpoint documentation for executing VGL prompts

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 Vgl AI skill do?

Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria Fibo models. Triggers on requests for structured prompts, controllab...

Why use Vgl on TypingMind?

Because you install it once and use it with any model. Vgl 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 Vgl in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Bria-AI/bria-skill/tree/dev/bria-ai-openclaw/skills/vgl. 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 Vgl?

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 Vgl?

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

Is the Vgl AI skill free?

Yes. It is published on GitHub by Bria-AI 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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