image-poster

name: image-posterdescription: |  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.triggers:   - "poster"  - "key art"  - "illustration"  - "image"  - "cover art"  - "海报"  - "插画"od:   mode: image  surface: image  scenario: design  preview:     type: html    entry: example.html  design_system:     requires: false  example_prompt: |    Editorial poster for an indie film festival — one bold abstract    silhouette over a warm, slightly grainy paper background; hand-set    sans serif title at the top, festival dates and venue at the bottom    in monospace. Muted ochre + ink palette.

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:

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:

"$OD_NODE_BIN" "$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 <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.