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Scenario Formats

Organization
scenario-labs
scenario-formats

Use when one approved visual, image or video, must ship at many sizes or placements through Scenario: adapting a master to 1:1, 4:5, 9:16, or 16:9, social formats for feeds and stories, YouTube thumbnails, storefront capsules and app store graphics, marketplace product images, link previews, display banners, a website hero, or choosing between crop, resize, expand, and generative reframe. Keywords: aspect ratio, resize, reframe, outpaint, expand, derivative, safe zone, thumbnail, banner.

Overview

Publisherscenario-labs
Repositoryskills
Skill namescenario-formats
Stars
970
Forks
114
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 scenario-labs on GitHub. Read the source before you install it.

Installation

Install the Scenario Formats 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/scenario-labs/skills/tree/main/skills/scenario-formats
  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/scenario-labs/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/scenario-formats .claude/skills/scenario-formats
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scenario Formats 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 Formats 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 Formats 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 Formats

Overview

Format adaptation is derivation, not regeneration: re-prompting the concept per placement makes five cousins, not five formats of one approved master. The failure modes are picking the wrong operation (a resize where canvas needed inventing, a crop that beheads the subject) and deriving in the wrong order. The canvas tools' exact contracts live in scenario-image-editing; this skill is the decision ladder and the order of operations around them, with per-placement ratios, pixel targets, and safe zones in references/placement-specs.md. A video master climbs the same ladder: video resize versus generative reframe lives in scenario-video-editing, and far ratios recompose per scenario-video. Connection and the core loop: see the scenario skill. Lettering per format: scenario-text-overlay. 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.

The decision ladder

Per target format, take the first rung that fits. Each rung is a model_run on a model found with search (target="models", public=true), never a dedicated MCP tool of its own:

The target needsOperation
Same ratio, other pixelsResize: exact, scales pixels, invents nothing
A tighter ratio, edges expendableResize with fit: "cover": exact, center-crops the overflow; only when the subject clears the cut
More canvas, composition intactExpand or uncrop: exact pixels, fills outward
Another ratio, recomposed around the subjectGenerative reframe; the text-protecting variant when lettering or marks are on the plate
A far ratio or a new visual hierarchyRecompose natively: a fresh run per scenario-image with the master as reference (scenario-consistency), composed for that ratio

The last rung applies more often than it looks: 16:9 key art seldom survives to 9:16 by any amount of outpainting, which is why the director siblings (scenario-video-ads) design each delivery ratio as its own composition.

Order of operations

  1. Approve one master first, at the largest clean size available, on a text-free plate (unattended, the task's named master or the brief's own description stands in for the approval).
  2. Derive every format from that master, never from another derivative: derivative-of-derivative compounds re-rendering artifacts.
  3. Reframe and expand before grading and grain: generative canvas work re-renders the image, so finishing passes applied first come back partly reinterpreted (scenario-image-editing holds the pipeline order). A video reframe also precedes any upscale: it renders at its own resolution tier whatever the input carries (1080p at most at authoring time), so an upscale ahead of it is discarded; upscale the reframed clip to delivery size.
  4. Verify by measurement before finishing: reframe ratio enums are approximate (a 4:5 request came back 29:36 at authoring time), so check output dimensions, land exact with a resize, and batch-check that subjects survived with asset_analyze (scenario-asset-analysis).
  5. Letter last: re-overlay lettering per format with scenario-text-overlay, sized to each final canvas, since type scaled by a resize turns soft and each placement has its own safe area. Keep critical content and text away from the edges platform UI covers: each placement's target and safe zone is in references/placement-specs.md as an authoring-time value, so confirm contractual specs with the user (unattended, task instructions win over the reference, and the delivery report states which was used).

Worked example: key art to story, feed, and thumbnail

  1. Master: 16:9 text-free key art, approved.
  2. 1:1 feed: generative reframe with a prompt naming what must stay ("the knight centered, both banners visible"), then a resize pass to the exact deliverable pixels.
  3. 9:16 story: a far ratio, so recompose natively: one run per scenario-image with the master as reference and a 9:16 composition clause; the master holds palette and subject on-look.
  4. Thumbnail: readability rules the crop, so reframe tight on the face or product, land exact with a resize, then asset_display the result and judge it small: thumbnails are seen at a tenth of their size.
  5. Verify dimensions against the deliverable list and batch-check that subjects survived with one asset_analyze pass, then overlay the title card per format onto each final canvas and file the set in a collection.

Common mistakes

  • Re-prompting per format: five generations of one prompt are five different images; derive from the master.
  • Stretching into a new ratio with a resize (fit: "stretch"): it distorts. A ratio change is fit: "cover" when the edges are expendable, else reframe, expand, or recomposition.
  • Grading and grain before reframing: the order is reshape, then grade, then texture.
  • Deriving from the lettered master: generative operations re-render type; keep a text-free plate and re-overlay per format.
  • Trusting a ratio enum: measure the output and resize to exact.
  • Center-cropping toward 9:16 because it is cheap: it is also how subjects lose their heads; the ladder exists to be climbed.
  • Skipping dry_run on reframes: generative canvas work is the expensive step in the chain, tens of times an effect's price (scenario-image-editing).

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 Scenario Formats AI skill do?

Use when one approved visual, image or video, must ship at many sizes or placements through Scenario: adapting a master to 1:1, 4:5, 9:16, or 16:9, social formats for feeds and stories, YouTube thumbnails, storefront capsules and app store graphics, marketplace product images, link previews, display banners, a website hero, or choosing between crop, resize, expand, and generative reframe. Keywords: aspect ratio, resize, reframe, outpaint, expand, derivative, safe zone, thumbnail, banner.

Why use Scenario Formats on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scenario-labs/skills/tree/main/skills/scenario-formats. 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 Scenario Formats?

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 Formats?

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

Is the Scenario Formats 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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