Agent skill

Scenario Seedream

by scenario-labs in scenario-labs/skills

A skill your agent uses when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact…

MITAuto-check passedMedia & Creative

Install Scenario Seedream

skills CLI
$ npx skills add scenario-labs/skills --skill scenario-seedream -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install scenario-labs/skills scenario-seedream --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scenario-seedream .claude/skills/scenario-seedream && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scenario-seedream
GitHub stars
946
Token cost
~2.2k tokens
SKILL.md length
1,157 words
Files
1
Skills in repo
146
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact…

  • Works in 7 steps: search with target="models",… → model_schema_get with that id: sizing… → model_run with that id, dry_run=true,… → …
  • Editing images with Seedream models on Scenario via MCP: text-to-image
  • SKILL.md covers Overview, Quick reference, Keeping a transparent asset… and Write the exact copy into the…, plus 3 more sections
  • Calls npx and uv

What it does

Scenario Seedream is an agent skill from scenario-labs/skills. Use when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact in-image text (non-Latin scripts too), subject-preserving edits, sets of related images in one run, editing a transparent asset without losing its alpha, or splitting an image into transparent PNG layers with Layerize. Keywords: Seedream 5.0 Pro, Lite, Flash, 4.5, Layerize, ByteDance, txt2img, img2img, layer extraction.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering Image generation and Image editing. It works with Model Context Protocol. The repository describes itself as: Get production-ready images, video, audio, and 3D from any AI agent: skills that pick the right model, price before spending, and keep characters and brands consistent through… The licence is MIT.

When your agent uses it

  • Editing images with Seedream models on Scenario via MCP: text-to-image
  • Image-to-image editing with reference images
  • Packaging with exact in-image text (non-Latin scripts too)
  • Subject-preserving edits

Example prompts

  • “/scenario-seedream”

Requirements

  • Node.js

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. search with target="models", query="seedream", public=true. Prefer the newest non-deprecated hit for the job, e.g…
  2. model_schema_get with that id: sizing fields, caps, defaults.
  3. model_run with that id, dry_run=true, and parameters={"prompt": "Concert poster, teal and cream, screen-print grain. Headline \"MIDNIGHT…
  4. Rerun model_run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout, never a second…
  5. asset_display and proofread the rendered text.
  6. model_schema_get on the Layerize hit, then model_run with that id and parameters={"image": "", "prompt": "Separate this poster into…
  7. jobs_wait, then asset_display each layer and asset_download the keepers.

What it can do on your machine

Read from SKILL.md and the folder at commit f6f8ab7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx and uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scenario Seedream loads about 2.2k tokens when it runs. Until then it costs about 128 tokens; SKILL.md has 1,157 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~128
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from scenario-labs/skills at commit f6f8ab7, republished under its MIT licence (© scenario-labs). 1,157 words, ~2,161 tokens.

Download SKILL.mdSave it as .claude/skills/scenario-seedream/SKILL.md (or your agent's skills folder).
name
scenario-seedream
description
Use when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact in-image text (non-Latin scripts too), subject-preserving edits, sets of related images in one run, editing a transparent asset without losing its alpha, or splitting an image into transparent PNG layers with Layerize. Keywords: Seedream 5.0 Pro, Lite, Flash, 4.5, Layerize, ByteDance, txt2img, img2img, layer extraction.
license
MIT

Scenario Seedream Images

Overview

Seedream, ByteDance's image family on Scenario, spans generation, reference-driven editing, and one member that only takes images apart: Layerize splits a finished image into editable layers. The members agree on little else, so discover them with search and read model_schema_get before every run.

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 (fields and caps are per member, read the live schema):

MemberReferencesSizingNotes
5.0 ProreferenceImages, up to 10exact width and height, 672 to 3136 px, step 16exact in-image text; 3500-char prompt
5.0 LitereferenceImages, up to 14width and height, up to 4Kfast and cheap; 2048-char prompt
5.0 FlashreferenceImages, up to 10exact width and height, 672 to 3136 px, step 16cheapest; background keeps a reference's alpha
4.5referenceImagessize (2K, 4K) plus aspectRatio enumsubject-preserving edits; "auto" ratio follows the source
5.0 Pro Layerizeone image, requiredsize tier: auto, 1K, 1.5K, 2Ksplits, never generates; prompt optional
5.0 Flash Layerizeone image, requiredsize tier: auto, 1K, 1.5K, 2Ksame contract minus optimizePromptMode

On the generators, mode follows from the inputs: empty referenceImages is text-to-image, one or more is an edit or a multi-reference generation (with several, the prompt gives each reference a role by position), and the array shape holds even for one asset. Sequence mode (sequentialImageGeneration: "auto" plus maxImages) lets Lite and 4.5 return a related set in one run, input plus generated capped at 15 images; Pro has no sequence fields. Price per image differs widely: a 2048 px square quoted 4 CU on Flash, 6 on Lite, and 18 on Pro at authoring time, and Pro took about two minutes against under one, so iterate on the cheap members and spend Pro on finals; dry_run both before a batch (the estimate prices the run exactly as submitted, a whole sequence included). Pro and Pro Layerize carry optimizePromptMode: the default standard reasons about the prompt first and is slower; fast costs the same and is usually enough when a reference already sets the composition (on Layerize it trades some split fidelity for speed). Flash's schema says width and height apply only when Resolution is Custom, but Flash has no Resolution field: the pixels apply directly.

Keeping a transparent asset transparent

Flash's background: "transparent" preserves alpha through an edit; it does not cut a subject out. It keeps the transparent surround, not translucency inside the subject: glass at alpha 170 came back near-opaque in one authoring-time test, so composite a translucent part in post. The field defaults to opaque, so a restyle that omits it flattens a valid cutout: set it on every run that must stay transparent. It applies only with exactly one reference image that already carries an alpha channel, and is ignored otherwise: a text-only run, a flattened JPEG reference, or two references all come back opaque without an error. So restyle or recolor an existing cutout (a sprite, an icon, a prop) on Flash with the PNG as the one reference, and check the result's alpha on the downloaded file before shipping it, since asset_display composites transparency away: with Pillow (uv run --with pillow when it is missing), Image.open(path).getchannel("A").getextrema() should start at 0 (fully transparent pixels exist), and an image with no A channel was flattened. To get a cutout in the first place, generate on a plain field and run background removal (the scenario-image-editing skill), or split the image with Layerize.

Write the exact copy into the prompt

Pro renders legible in-image text, multi-line layouts and non-Latin scripts included. Quote the exact strings in the prompt instead of paraphrasing them, give each a position and a size rank (headline across the top, date line small at the foot), and keep to a few elements at one or two sizes: letters are drawn, not typeset, so paragraphs and many small labels turn to shapes, and copy past that budget (tour dates, credits) is composited in post from the start. Proofread with asset_display; a word that garbles is spelled out letter by letter after the quoted string on the rerun, and one that still garbles goes to post. To swap copy on a reference, quote the new string, pin its position, and require the original typeface, size and color with everything else unchanged.

Show full SKILL.md (400 more words)Show less

Layerize: one image in, an editable stack out

Layerize returns a base layer plus up to 16 transparent PNG cutouts, rebuilding the background behind whatever it lifts. The prompt picks the mode: empty runs a full automatic split; an enumerated list of parts ("Separate this poster into transparent layers: headline, product, shadow, background") cuts better than "all layers"; <bbox>x1 y1 x2 y2</bbox> on a 0 to 1000 grid, origin top left, confines the split to one region. Set size explicitly, since auto inherits the source's tier and the tier moves the price; cost is otherwise flat per run, not per layer. Layers come back cropped to their own bounds with bbox and z-index metadata, not aligned to the source canvas, and there is no PSD export.

Worked example: a poster, then its layers

  1. search with target="models", query="seedream", public=true. Prefer the newest non-deprecated hit for the job, e.g. model_bytedance-seedream-5-0-pro (a live hit at authoring time: re-discover each session).
  2. model_schema_get with that id: sizing fields, caps, defaults.
  3. model_run with that id, dry_run=true, and parameters={"prompt": "Concert poster, teal and cream, screen-print grain. Headline \"MIDNIGHT ORBIT\" across the top, date line \"Nov 14, Union Hall\" small at the foot.", "width": 1600, "height": 2368}; both sizing fields move price.
  4. Rerun 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.
  5. asset_display and proofread the rendered text.
  6. model_schema_get on the Layerize hit, then model_run with that id and parameters={"image": "<poster asset id>", "prompt": "Separate this poster into transparent layers: the headline text, the date line, and the background. Clean edges, complete transparency.", "size": "2K"}.
  7. jobs_wait, then asset_display each layer and asset_download the keepers.

Common mistakes

  • Reusing one member's parameter block on another: pixels on Pro and Lite, tier enums on 4.5 and Layerize; pixels sent to an enum field are rejected.
  • Passing referenceImages to Layerize or image to the generators: the input field's name and shape differ per member.
  • Expecting Layerize layers to overlay the source directly: each is cropped to its own bounds, so reposition with the returned metadata.
  • Asking Pro for a sequence: the sequence fields existed on Lite and 4.5 only.
  • Moving a 3000-character prompt from Pro to Lite: prompt caps are per member.
  • Setting Flash's background to transparent on a text-only run or a flattened reference: it is silently ignored, and the image comes back opaque.

© scenario-labs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/scenario-seedream of scenario-labs/skills.

Open the folder on GitHubat commit f6f8ab7

Compare with similar skills

Scenario Seedream next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scenario Seedream compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scenario Seedream this skillscenario-labs/skills946—~2.2kAutomated safety check: PassMIT
Canva Designerasgeirtj/system_prompts_leaks69k—~2.1kAutomated safety check: PassCC0-1.0
Olore Recraft Latestolorehq/olore104—~773Automated safety check: PassMIT
Nano Banana Proswarmclawai/swarmclaw689—~481Automated safety check: PassMIT
Imageguaardvark/guaardvark258—~1.8kAutomated safety check: PassMIT
AI Image Generation and Editingzhayujie/CowAgent47k—~1.3kAutomated safety check: PassMIT

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Questions about Scenario Seedream

What does Scenario Seedream do?

A skill your agent uses when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact…. Scenario Seedream is an agent skill from scenario-labs/skills. Use when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact in-image text (non-Latin scripts too), subject-preserving edits, sets of related images in one run, editing a transparent asset without losing its alpha, or splitting an image into transparent PNG layers with Layerize.

When should I use Scenario Seedream?

Scenario Seedream fits situations like: editing images with Seedream models on Scenario via MCP: text-to-image; image-to-image editing with reference images; packaging with exact in-image text (non-Latin scripts too); subject-preserving edits.

How do I install Scenario Seedream in Claude Code?

Run `npx skills add scenario-labs/skills --skill scenario-seedream -a claude-code`. Or copy the skill folder (skills/scenario-seedream in scenario-labs/skills) into .claude/skills/scenario-seedream in your project. Claude Code loads it when a task matches its description.

How do I install Scenario Seedream in Codex?

Run `npx skills add scenario-labs/skills --skill scenario-seedream -a codex`. Or copy the skill folder (skills/scenario-seedream in scenario-labs/skills) into .agents/skills/scenario-seedream in your project. Codex loads it when a task matches its description.

Can I use Scenario Seedream in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add scenario-labs/skills --skill scenario-seedream -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scenario-seedream, .gemini/skills/scenario-seedream, .github/skills/scenario-seedream and .opencode/skills/scenario-seedream in your project.

What does Scenario Seedream need to run?

Going by SKILL.md and its folder, Scenario Seedream needs the command-line tools its instructions call (npx and uv). Our summary lists: Node.js.

Does Scenario Seedream access the network?

SKILL.md contains no URLs. Its commands use npx and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Scenario Seedream safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scenario Seedream use?

Scenario Seedream is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scenario Seedream use?

About 2.2k tokens (SKILL.md is roughly 8.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scenario Seedream?

Skills that share tags, products or a category with Scenario Seedream: Canva Designer (asgeirtj/system_prompts_leaks, 69k stars), Olore Recraft Latest (olorehq/olore, 104 stars), Nano Banana Pro (swarmclawai/swarmclaw, 689 stars) and Image (guaardvark/guaardvark, 258 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scenario Seedream?

scenario-labs (a GitHub organization) maintains it in scenario-labs/skills, which has 946 GitHub stars. The repository holds 146 skills in this directory. The repository was last updated on October 10, 2026.

Source: scenario-labs/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.