image-poster

nexu-io/open-design · Agent Skill

為海報、關鍵藝術和插圖生成影象,並利用各種可選模型。

前往安裝 ↓

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發布者nexu-io組織
儲存庫nexu-io/open-design ↗
技能資料夾image-poster
星95,886
分支11,122
附帶檔案2
授權Apache-2.0
最近更新2026年9月13日
連結技能原始碼 ↗ · 儲存庫 ↗

Mozgi 檢查

我們在技能檔案中發現的內容。詳細資訊需訂閱。

安全性
低風險 · 未發現明顯風險 — 完整報告會顯示細節
相容性
僅含說明 — 適用於多數代理
相似技能
找到 37 個相似技能
訂閱另含

安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。

把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。

Install the “image-poster” skill from mozgi.io (https://mozgi.io/skills/image-poster/) for me.

1. Download the archive: https://mozgi.io/skills/image-poster/download
   If it fails, take the skill folder from the source: https://github.com/nexu-io/open-design/tree/main/design-templates/image-poster
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “image-poster” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/image-poster/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

技能檔案

2 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

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.

常見問題

image-poster 技能有什麼用途?

為海報、關鍵藝術和插圖生成影象,並利用各種可選模型。

如何安裝 image-poster?

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。

image-poster 適用哪些 AI 模型?

技能是純 Markdown 指示(Agent Skills 格式),因此 image-poster 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。

image-poster 是免費的嗎?

此技能由 nexu-io 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎?

技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同?

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

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