image-poster

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\nsilhouette over a warm, slightly grainy paper background; hand-set\nsans serif title at the top, festival dates and venue at the bottom\nin monospace. Muted ochre + ink palette.\n"} 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: Subject + composition — what is in the frame, where, at what scale; eye-line and crop. Lighting + mood — natural / studio / moody; warm / cool; key plus rim plus fill; time of day if outdoor. Palette + textures — hex anchors when the user gave a brand palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink"). Camera / lens — only if the user wants photographic realism ("85mm portrait, shallow DOF") or a specific film stock. 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.