image-poster

Original：🇺🇸 English
Translated

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.

7installs
Sourcenexu-io/open-design
Added on

NPX Install

npx skill4agent add nexu-io/open-design image-poster

Tags

Translated version includes tags in frontmatter
image-generationposter-designai-artprompt-engineeringmedia-workflow

SKILL.md Content

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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; only ask the user to fill them in if they're marked 
(unknown — ask)
.

Step 1 — Compose the prompt

Plan in this exact order before calling any tool:
  1. Subject + composition — what is in the frame, where, at what scale; eye-line and crop.
  2. Lighting + mood — natural / studio / moody; warm / cool; key plus rim plus fill; time of day if outdoor.
  3. Palette + textures — hex anchors when the user gave a brand palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").
  4. Camera / lens — only if the user wants photographic realism ("85mm portrait, shallow DOF") or a specific film stock.
  5. What to avoid — common AI-slop patterns ("no extra fingers, no warped text, no logo placeholders").

Step 2 — Dispatch via the media contract

Use the unified dispatcher — do not call upstream provider APIs by hand. Run from your shell tool:
bash
node "$OD_BIN" media generate \
  --project "$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 
<artifact>
tag.

Hard rules

  • One image per turn unless asked for variations.
  • Honor 
    imageAspect
    exactly — the upstream cost is the same; matching the aspect avoids a re-render.
  • No filler typography in the image itself unless the user asked for in-frame text. Real copy beats lorem.
  • Save every render — never describe an image without producing the file. The user expects something to open in the file viewer.