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Agent skill

Scenario Luma Image

by scenario-labs in scenario-labs/skills

A skill your agent uses when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images…

MITAuto-check passedMedia & Creative

Install Scenario Luma Image

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

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

GitHub CLI
$ gh skill install scenario-labs/skills scenario-luma-image --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-luma-image .claude/skills/scenario-luma-image && 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-luma-image
GitHub stars
946
Token cost
~1.5k tokens
SKILL.md length
820 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 Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images…

  • Works in 6 steps: search with target="models", query="luma… → model_schema_get with that id: reference… → upload_asset the palette reference (see… → …
  • Editing images with Luma Uni-1 models on Scenario via MCP: text-to-image
  • SKILL.md covers Overview, Quick reference, References work by role and Create prompts describe, edit…, plus 2 more sections
  • Calls npx

What it does

Scenario Luma Image is an agent skill from scenario-labs/skills. Use when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.

Its SKILL.md is about 1.5k 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 Web search. 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 Luma Uni-1 models on Scenario via MCP: text-to-image
  • Prompt-based editing of an existing image
  • Character reference images with named roles
  • Web search grounding for real-world subjects

Example prompts

  • “/scenario-luma-image”

Requirements

  • Node.js

Workflow steps

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

  1. search with target="models", query="luma uni", public=true. Prefer the newest non-deprecated hit, e.g. model_luma-uni-1-max (a live hit at…
  2. model_schema_get with that id: reference cap, ratio list, and defaults before anything else.
  3. upload_asset the palette reference (see the scenario skill) to get its asset id.
  4. model_run with that model_id, dry_run=true, and the exact parameters={"prompt": "A travel poster of Kyoto in autumn, a pagoda above red…
  5. Repeat model_run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout. The model reasons…
  6. asset_display to check the title spelling and palette, then asset_download to save.

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

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

  • Network

    No URLs in SKILL.md. Its commands use npx, 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 Luma Image loads about 1.5k tokens when it runs. Until then it costs about 120 tokens; SKILL.md has 820 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~120
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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). 820 words, ~1,540 tokens.

Download SKILL.mdSave it as .claude/skills/scenario-luma-image/SKILL.md (or your agent's skills folder).
name
scenario-luma-image
description
Use when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.
license
MIT

Scenario Luma Image

Overview

Uni-1, Luma Labs' image family on Scenario, folds generation and editing into one contract: every member is both txt2img and img2img, and passing a source image is what flips the run into edit mode, so where each image lands (source versus reference) decides more than prompt wording. These are reasoning models that plan lighting and composition before rendering, so a run takes a minute or two, not seconds. Discover members with search and treat model_schema_get as the contract: the tiers share every field name and disagree on caps and price. Luma's video models are the scenario-luma-video skill's domain.

Connection and the core loop: see the scenario skill in this repo; model-agnostic image work (sizing families, masks, batch fields): 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

Mode follows from the inputs (names from the live schema):

ModeInputsBehavior
Createprompt (+ imageRef)full scene from the prompt; aspectRatio honored, default 3:2
Editsource + prompt (+ imageRef)prompt states the change; output keeps the source ratio unless aspectRatio is set

imageRef is an array of guiding images and combines with source. Caps are per member: at authoring time the Max hit took 9 references and the standard 8, with the source occupying one of those slots when editing; both took a 6000 character prompt, nine aspectRatio values from 1:3 to 3:1, and outputFormat png (default) or jpeg. Each reference adds cost, so re-estimate with dry_run after changing the count. webSearch (default false) has the model fetch real-world visuals before generating: enable it when the prompt names a real place, product, or style the model may not know. No seed, mask, pixel-size, or batch-count field exists: sizing is the ratio alone, masked edits belong to other models, and identical re-runs cannot be pinned, so change one thing per iteration. At authoring time the Max tier cost roughly two and a half times the standard for one 2K image despite near-identical public arena ratings, so dry_run the same job on both before a batch.

References work by role

The model follows a reference reliably only when the prompt says what to take from it: character likeness, style, composition, color palette, lighting, texture, or mood. Unlabeled references get guessed at, and there is no adherence slider; influence rises with prompt specificity ("use the first reference for the exact colorway and stitching"). Roles stack across references, one each. Reusing one canonical reference across iterations is what holds a character steady.

Create prompts describe, edit prompts preserve

Create prompts read as one scene in natural prose: subject, setting, lighting, mood, style, and always name the lighting, the single biggest quality lever. For text in the image, put the exact string in quotes; rendered text is a family strength, and the Max tier's advertised edge is accurate non-Latin scripts, not Latin text generally. Edit prompts are surgical: state the change first, then pin what must not move ("Change X to Y. Keep Z exactly as it is."). One scene or one change per run.

Show full SKILL.md (290 more words)Show less

Worked example: a travel poster with rendered title text

  1. search with target="models", query="luma uni", public=true. Prefer the newest non-deprecated hit, e.g. model_luma-uni-1-max (a live hit at authoring time: re-discover each session).
  2. model_schema_get with that id: reference cap, ratio list, and defaults before anything else.
  3. upload_asset the palette reference (see the scenario skill) to get its asset id.
  4. model_run with that model_id, dry_run=true, and the exact parameters={"prompt": "A travel poster of Kyoto in autumn, a pagoda above red maples, warm golden hour light, flat-print texture. Use the reference for color palette and print grain. The title text \"KYOTO\" in bold serif across the top.", "imageRef": ["asset_a"], "aspectRatio": "2:3", "webSearch": true} (a real place is named, so ground it).
  5. Repeat model_run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout. The model reasons before rendering (median latency near 90 seconds at authoring time), so a timeout is not a failure and never justifies a second model_run.
  6. asset_display to check the title spelling and palette, then asset_download to save.

Common mistakes

  • A style reference passed as source: that flips the run into edit mode and the output hugs the reference. References go in imageRef; source is only the image being changed.
  • Unlabeled references: name each one's role in the prompt or the model guesses which to follow.
  • An edit prompt with no preservation clause: whatever is not pinned is fair game.
  • Expecting a square by default: create mode defaults to 3:2, so set aspectRatio explicitly.
  • Hunting for seed, mask, width, or a batch count: none exist at authoring time; read model_schema_get instead of assuming.
  • Carrying the Max member's reference cap or price to the standard one: they share field names, not numbers.

© 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-luma-image of scenario-labs/skills.

Open the folder on GitHubat commit f6f8ab7

Compare with similar skills

Scenario Luma Image 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 Luma Image compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scenario Luma Image this skillscenario-labs/skills946—~1.5kAutomated safety check: PassMIT
BlockrunBlockRunAI/blockrun-mcp391—~2.7kAutomated safety check: PassMIT
Ag2 Use Builtin Toolsag2ai/build-with-ag2252—~1.3kAutomated safety check: PassApache-2.0
SEO Image GeneratorAgriciDaniel/claude-seo19k2 repos~2.1kAutomated safety check: PassMIT
Gauntlet Loopduolahypercho/gauntlet-loop165—~689Automated safety check: PassMIT
Blog ImageAgriciDaniel/claude-blog2.3k—~3.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Scenario Luma Image

What does Scenario Luma Image do?

A skill your agent uses when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images…. Scenario Luma Image is an agent skill from scenario-labs/skills. Use when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1.

When should I use Scenario Luma Image?

Scenario Luma Image fits situations like: editing images with Luma Uni-1 models on Scenario via MCP: text-to-image; prompt-based editing of an existing image; character reference images with named roles; web search grounding for real-world subjects.

How do I install Scenario Luma Image in Claude Code?

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

How do I install Scenario Luma Image in Codex?

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

Can I use Scenario Luma Image 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-luma-image -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-luma-image, .gemini/skills/scenario-luma-image, .github/skills/scenario-luma-image and .opencode/skills/scenario-luma-image in your project.

What does Scenario Luma Image need to run?

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

Does Scenario Luma Image access the network?

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

Is Scenario Luma Image 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 Luma Image use?

Scenario Luma Image 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 Luma Image use?

About 1.5k tokens (SKILL.md is roughly 6.2k 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 Luma Image?

Skills that share tags, products or a category with Scenario Luma Image: Blockrun (BlockRunAI/blockrun-mcp, 391 stars), Ag2 Use Builtin Tools (ag2ai/build-with-ag2, 252 stars), SEO Image Generator (AgriciDaniel/claude-seo, 19k stars) and Gauntlet Loop (duolahypercho/gauntlet-loop, 165 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scenario Luma Image?

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.