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image-poster

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.

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.

安装中心

可直接安装到 9 个框架(另有 Cursor / Windsurf 需转换为 rules 格式)。悬停可查看各框架的技能目录。

方式一 · 复制提示词(推荐)

粘贴给你的 Claude Code,它会自己完成下载和安装(安装文件直接取自原始仓库)

请帮我安装技能「image-poster」:
1. 从原始仓库下载技能文件:https://raw.githubusercontent.com/nexu-io/open-design/main/design-templates/image-poster/SKILL.md(GitHub 上的 SKILL.md 原始文件)
2. 保存为 ~/.claude/skills/image-poster/SKILL.md(目录不存在则创建)
3. 确认文件存在后,告诉我安装结果

方式二 · 命令行安装

npx CLI 跨平台可用(自动检测本机 Agent);PowerShell 方式无需安装任何东西

npx ailine-skills add image-poster
New-Item -ItemType Directory -Force "$HOME\.claude\skills\image-poster" | Out-Null; curl.exe -fsSL "https://raw.githubusercontent.com/nexu-io/open-design/main/design-templates/image-poster/SKILL.md" -o "$HOME\.claude\skills\image-poster\SKILL.md"

方式三 · 前往原始仓库

第三方技能不由本站分发安装文件,请从原始仓库获取(上方命令/提示词已直连原始文件)