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skills/image-poster/SKILL.md

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+---
+name: image-poster
+description: |
+  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; 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.