image-poster
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.
What this skill does
# 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
"$OD_NODE_BIN" "$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.
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