Scenario Grok Imagine Image

Organization

scenario-labs

scenario-grok-imagine-image

Use when generating or editing images with Grok Imagine models on Scenario via MCP: text-to-image, instruction editing with up to three reference images, posters, packaging, ads, or infographics with exact in-image text, photoreal shots from camera and lighting cues, extended aspect ratios up to 20:9 and 9:20, quality and resolution tiers, or batches of variations. Keywords: Grok Imagine Image 2.0, Image Quality, xAI, Aurora, Grok, txt2img, img2img, typography, 2K.

Overview

Publisher	scenario-labs
Repository	`skills`
Skill name	`scenario-grok-imagine-image`
Stars	970
Forks	114
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by scenario-labs on GitHub. Read the source before you install it.

Installation

Install the Scenario Grok Imagine Image AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/scenario-labs/skills/tree/main/skills/scenario-grok-imagine-image
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.

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/scenario-labs/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/scenario-grok-imagine-image .claude/skills/scenario-grok-imagine-image

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scenario Grok Imagine Image 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 Scenario Grok Imagine Image 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 Scenario Grok Imagine Image 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.

Scenario Grok Imagine Images

Overview

Grok Imagine, xAI's image family on Scenario, puts generation and editing in every member: the same model reads an empty referenceImages as text-to-image and a filled one as an edit, so the inputs pick the mode and the member picks the tier. Members ship side by side, agreeing on the prompt contract and disagreeing on knobs and price, so discover them with search and treat model_schema_get as the contract. The Grok Imagine video members belong to the scenario-grok-imagine-video skill.

Connection and the core loop: see the scenario skill; model-agnostic image work: the scenario-image skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

At authoring time (caps are per member, read the live schema):

Member	Aspect ratios	Notes
2.0	14 values up to 20:9 and 9:20, default auto	adds `quality` (low, medium); strongest text and edits
Quality	same 14, default 1:1	fast at 2k; price near flat across variants
Image	10 values, 2:1 through 1:2	cheapest per image

The rest is shared: prompt up to 10000 characters, referenceImages capped at 3, numOutputs 1 to 10, resolution at 1k or 2k. No seed, mask, negative prompt, or pixel sizing exists anywhere in the family: size is ratio times resolution tier, and aspectRatio decides what is in frame, not just the crop. In editing mode aspectRatio is ignored entirely and the output follows the source's proportions. The price spread is why the older members stay live: one image on the unversioned member cost a fraction of a versioned run at authoring time, and 2.0's 2k text-to-image ran well over a minute where the others took seconds. So iterate cheap and fast (the unversioned member, or 2.0 at quality: "low" and 1k), spend a medium 2k run on keepers, and dry_run two members on the same job before a batch: numOutputs, quality, resolution, and references all move price.

Write the copy, not a vibe

The family's edge is legible in-image text: posters, packaging, infographics, memes. It only pays when the prompt carries the copy verbatim: wrap every literal string in quotes, give each a role ("the title reads ...", "a line beneath reads ..."), keep strings short, and close with "no other text", or the model invents extra labels. Real numbers and statistics go in as quoted copy too, since invented figures look plausible. Render small type at 2k and proofread in asset_display before building on the result. For the rest, write a short natural-language brief, subject, setting, composition, light, finish, in 30 to 80 words: quality adjectives ("8K", "masterpiece") are ignored and negatives mostly too, so state positives ("clean empty background", not "no clutter") and keep one aesthetic per prompt.

Edits are instructions, no mask exists

Editing is prompt-driven: pass up to three referenceImages and describe the change. Two habits keep it surgical: describe only what changes and name what stays ("keep the pose, background, and lighting unchanged"), and chain one edit per run instead of stacking several. With more than one reference, state each image's role (subject, style, scene) so the blend lands where intended. The output inherits the source's proportions, so reframe before the edit, never with aspectRatio.

Worked example: a branded label onto a product photo

search with target="models", query="grok imagine image", public=true. Prefer the newest non-deprecated hit, e.g. model_xai-grok-imagine-image-2-0 (a live hit at authoring time: re-discover each session).
model_schema_get with that id: the reference cap, quality and resolution values, and the ratio list before anything else.
upload_asset the product photo (see the scenario skill) to get its asset id.
model_run with that model_id, dry_run=true, and the exact parameters={"prompt": "Add a minimalist front label to the bottle where the title reads \"LUMEN\" in a clean white serif and a smaller line beneath reads \"Sparkling Citrus Soda\". Keep the bottle shape, glass tint, background, shadows, and lighting unchanged, no other text.", "referenceImages": ["asset_abc"], "quality": "medium", "resolution": "2k"}; re-estimate after any change to quality, resolution, numOutputs, or references.
Repeat model_run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout, never a second model_run.
asset_display and proofread the label at full size, then asset_download the keeper.

Common mistakes

Setting aspectRatio on an edit: it is ignored and the output keeps the source's proportions; reframe the source first.
Describing copy loosely ("a catchy tagline"): text comes back garbled or invented; quote exact strings and end with "no other text".
Negative prompts: largely ignored; state the positive instead.
A bare string where the schema says array: one reference still goes as ["asset_x"].
Carrying quality beyond 2.0: at authoring time no other member had the field; check the live schema before reusing parameters across members.
Fine print at 1k: small type blurs; render text-heavy finals at 2k.

Frequently asked questions

What does the Scenario Grok Imagine Image AI skill do?

Why use Scenario Grok Imagine Image on TypingMind?

Because you install it once and use it with any model. Scenario Grok Imagine Image 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 Scenario Grok Imagine Image in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scenario-labs/skills/tree/main/skills/scenario-grok-imagine-image. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scenario Grok Imagine Image?

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 Scenario Grok Imagine Image?

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

Is the Scenario Grok Imagine Image AI skill free?

Yes. It is published on GitHub by scenario-labs 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Scenario Grok Imagine Images

Overview

Quick reference

Write the copy, not a vibe

Edits are instructions, no mask exists

Worked example: a branded label onto a product photo

Common mistakes

Frequently asked questions

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