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105 lines
3.4 KiB
Markdown
105 lines
3.4 KiB
Markdown
---
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name: image-poster
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description: |
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Single-image generation skill for posters, key art, and editorial
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illustrations. Defaults to gpt-image-2 but is provider-agnostic — the
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same workflow drives Flux, Imagen, or Midjourney via the active
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upstream tooling. Output is one or more PNG/JPEG files saved to the
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project folder.
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triggers:
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- "poster"
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- "key art"
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- "illustration"
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- "image"
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- "cover art"
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- "海报"
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- "插画"
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od:
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mode: image
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surface: image
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scenario: design
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preview:
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type: html
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entry: example.html
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design_system:
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requires: false
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example_prompt: |
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Editorial poster for an indie film festival — one bold abstract
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silhouette over a warm, slightly grainy paper background; hand-set
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sans serif title at the top, festival dates and venue at the bottom
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in monospace. Muted ochre + ink palette.
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---
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# Image Poster Skill
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Produce **one** finished image asset per turn unless the user asks for
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variations. Image generation rewards a tight, structured prompt — your
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job is to assemble that prompt from the user's brief, then dispatch.
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## Resource map
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```
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image-poster/
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├── SKILL.md ← you're reading this
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└── example.html ← what the resulting card looks like in Examples
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```
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## Workflow
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### Step 0 — Read the project metadata
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The active project carries `imageModel`, `imageAspect`, and (optional)
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`imageStyle` notes. Use them as the upstream model + canvas + style
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anchor; only ask the user to fill them in if they're marked `(unknown
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— ask)`.
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### Step 1 — Compose the prompt
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Plan in this exact order before calling any tool:
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1. **Subject + composition** — what is in the frame, where, at what
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scale; eye-line and crop.
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2. **Lighting + mood** — natural / studio / moody; warm / cool; key
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plus rim plus fill; time of day if outdoor.
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3. **Palette + textures** — hex anchors when the user gave a brand
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palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").
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4. **Camera / lens** — only if the user wants photographic realism
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("85mm portrait, shallow DOF") or a specific film stock.
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5. **What to avoid** — common AI-slop patterns ("no extra fingers, no
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warped text, no logo placeholders").
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### Step 2 — Dispatch via the media contract
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Use the unified dispatcher — do **not** call upstream provider APIs by
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hand. Run from your shell tool:
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```bash
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node "$OD_BIN" media generate \
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--project "$OD_PROJECT_ID" \
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--surface image \
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--model "<imageModel from metadata>" \
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--aspect "<imageAspect from metadata>" \
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--output "<short-descriptive-name>.png" \
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--prompt "<the full assembled prompt from Step 1>"
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```
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The command prints one line of JSON: `{"file": {"name": "...", ...}}`.
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The daemon writes the bytes into the project folder; the FileViewer
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picks it up automatically.
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### Step 3 — Hand off
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Reply with a one-paragraph summary of the prompt you used and the
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filename returned by the dispatcher (e.g. *I generated `hero-poster.png`
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with `gpt-image-2` at 1:1.*). Do **not** emit an `<artifact>` tag.
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## Hard rules
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- One image per turn unless asked for variations.
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- Honor `imageAspect` exactly — the upstream cost is the same; matching
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the aspect avoids a re-render.
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- No filler typography in the image itself unless the user asked for
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in-frame text. Real copy beats lorem.
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- Save every render — never describe an image without producing the
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file. The user expects something to open in the file viewer.
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