Mmcp.market

image-poster skill

by nexu-io·nexu-io/open-design·98k stars·Apache-2.0

Single-image generation skill for posters, key art, and editorial illustrations. Defaults to gpt-image-2 but is provider-agnostic — the same workflow drives Flux, Imagen, or Midjourney via the active upstream tooling. Output is one or more PNG/JPEG files saved to the project folder.

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Install the image-poster skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/nexu-io/open-design.git /tmp/open-design
mkdir -p ~/.claude/skills
cp -r /tmp/open-design/design-templates/image-poster ~/.claude/skills/image-poster
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Image Poster Skill

Produce one finished image asset per turn unless the user asks for variations. Image generation rewards a tight, structured prompt — your job is to assemble that prompt from the user's brief, then dispatch.

Resource map

image-poster/
├── SKILL.md         ← you're reading this
└── example.html     ← what the resulting card looks like in Examples

Workflow

Step 0 — Read the project metadata

The active project carries imageModel, imageAspect, and (optional) imageStyle notes. Use them as the upstream model + canvas + style anchor. When a value is not provided, infer a safe default from the brief and media contract. Ask only when the choice would materially change the requested result and no safe default can be inferred.

Step 1 — Compose the prompt

Plan in this exact order before calling any tool:

scale; eye-line and crop.

  1. Subject + composition — what is in the frame, where, at what

plus rim plus fill; time of day if outdoor.

  1. Lighting + mood — natural / studio / moody; warm / cool; key

palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").

  1. Palette + textures — hex anchors when the user gave a brand

("85mm portrait, shallow DOF") or a specific film stock.

  1. Camera / lens — only if the user wants photographic realism

warped text, no logo placeholders").

  1. What to avoid — common AI-slop patterns ("no extra fingers, no

Step 2 — Dispatch via the media contract

Use the unified dispatcher — do not call upstream provider APIs by hand. Run from your shell tool:

"$OD_NODE_BIN" "$OD_BIN" media generate \
  --project "$OD_PROJECT_ID" \
  --surface image \
  --model "<imageModel from metadata>" \
  --aspect "<imageAspect from metadata>" \
  --output "<short-descriptive-name>.png" \
  --prompt "<the full assembled prompt from Step 1>"

The command prints one line of JSON: {"file": {"name": "...", ...}}. The daemon writes the bytes into the project folder; the FileViewer picks it up automatically.

Step 3 — Hand off

Reply with a one-paragraph summary of the prompt you used and the filename returned by the dispatcher (e.g. I generated hero-poster.png with gpt-image-2 at 1:1.). Do not emit an tag.

Hard rules

the aspect avoids a re-render.

  • One image per turn unless asked for variations.
  • Honor imageAspect exactly — the upstream cost is the same; matching

in-frame text. Real copy beats lorem.

  • No filler typography in the image itself unless the user asked for

file. The user expects something to open in the file viewer.

  • Save every render — never describe an image without producing the

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