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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…

Install / Use

npx skills add scenario-labs/skills --skill scenario-formats

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

82/100

Supported Platforms

Universal

Our assessment of scenario-formats

scenario-formats scores 82/100 on our quality scale, 3370th of 4,647 Development & Engineering skills we index.

Its SKILL.md is 6.6 KB long, split into 6 sections and no code examples: a thorough specification that gives an agent plenty to work with.

With 1,048 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
11/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 8 days ago, so scenario-formats is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

scenario-formats compared with similar skills

All 4 of these similar skills score higher than scenario-formats; compare them before choosing.

SkillScoreStarsUpdatedFormat
scenario-formats (this skill)by scenario-labs821.0k8d agoSKILL.md
LocalAIby mudler10050.0ktodayMCP Server
algorithmic-artby anthropics100177.9k34d agoSKILL.md
pptxby anthropics100177.9k34d agoSKILL.md
math-olympiadby anthropics10043.1k8d agoSKILL.md

Frequently asked questions

How do I install scenario-formats?
Run npx skills add scenario-labs/skills --skill scenario-formats. The install tabs above show the steps for each supported agent.
Which AI agents does scenario-formats work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is scenario-formats safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is scenario-formats still maintained?
The repository was last updated 8 days ago, so scenario-formats is actively maintained.

name: scenario-formats description: "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." license: MIT

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 needs | Operation | | -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | Same ratio, other pixels | Resize: exact, scales pixels, invents nothing | | A tighter ratio, edges expendable | Resize with fit: "cover": exact, center-crops the overflow; only when the subject clears the cut | | More canvas, composition intact | Expand or uncrop: exact pixels, fills outward | | Another ratio, recomposed around the subject | Generative reframe; the text-protecting variant when lettering or marks are on the plate | | A far ratio or a new visual hierarchy | Recompose 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).

Related Skills

View on GitHub
GitHub Stars1.0k
CategoryDevelopment
Updated8d ago
Forks129

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions
scenario-formats — Universal Skill: Install & Safety Check | SkillAgent