Xpersona
agentCLAWHUBUnverified

商品详情图生成 Item Detail

生成带中文排版的详情页模块。商品图 + 卖点 → 可直接上架的详情页图文模块。当用户说「详情页」「做详情图」「商品描述图」「详情页模块」时使用。 Skill: 商品详情图生成 Item Detail Owner: dlazyai Summary: 生成带中文排版的详情页模块。商品图 + 卖点 → 可直接上架的详情页图文模块。当用户说「详情页」「做详情图」「商品描述图」「详情页模块」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:51:40.654Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:44:15.698Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:44:14.832Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:16:47.405Z | user 例行版本更新 2026-10-02 v1.0.15 | 2026-09-3

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

1.0.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.3K downloadsadoption · observed Oct 10, 2026
Latest release
1.0.19release · observed Oct 10, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:item-detail
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-detail/snapshot"

Documentation

CLAWHUB

146,913 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: item-detail
version: 1.0.19
description: 生成带中文排版的详情页模块。商品图 + 卖点 → 可直接上架的详情页图文模块。当用户说「详情页」「做详情图」「商品描述图」「详情页模块」时使用。
---

# item-detail — 一键生成全套商品详情图

一张商品图 + 卖点文案 → **带中文排版的详情页视觉**。

和其他技能的区别：本技能的产出**带文字**。所以模型选型的第一标准是**中文字形渲染能力**——大多数图像模型会把中文画成乱码。

---

## 生成效果示例

| 输入：商品图 |
| --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/item-detail/product-flatlay.jpg" width="280"> |
| `product-flatlay.jpg` — 军绿麻花针织毛衣平铺图，800×800 |

实际执行的命令（首屏 banner 模块）：

```bash
dlazy seedream-5.0-pro \
  --prompt '电商服饰详情页首屏 banner，竖版。左侧是图1 中的军绿色麻花针织圆领毛衣的模特上身图（青年男模特，半身，落肩宽松版型），毛衣颜色、麻花织法与菱形提花必须与图1一致。右上方留白区排版中文标题，大字「粗棒麻花 复古落肩」，副标题小字「羊毛混纺 · 加厚保暖 · 男女同款」。底部一行三个圆形图标配文字「亲肤不扎」「不易变形」「机洗不缩」。整体米灰色背景，暖色调，留白克制，字体为无衬线黑体，排版整齐对齐，商业电商详情页设计感，中文字必须清晰正确无乱码。' \
  --images docs/item-detail/product-flatlay.jpg --size 3:4 \
  --save docs/item-detail/example-output.jpg
```

**输出**

<img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/item-detail/example-output.jpg" width="320">

`example-output.jpg` — 3:4 / 2K，5 credits。模特上身图占左侧，毛衣的军绿色、麻花与菱形提花保留；右上标题「粗棒麻花 复古落肩」与副标题「羊毛混纺 · 加厚保暖 · 男女同款」字形正确、层级分明；底部三个线描图标配「亲肤不扎」「不易变形」「机洗不缩」，米灰底暖调，排版对齐。

---

## 1、能力边界

| 产出模块 | 说明 |
| --- | --- |
| 首屏 banner | 模特上身图 / 商品图 + 大标题 + 副标题 |
| 卖点图标行 | 3~4 个圆形图标 + 短文案 |
| 材质说明块 | 面料/材质特写 + 说明文字 |
| 细节展示块 | 局部特写 + 工艺说明 |
| 参数块 | 尺码表 / 规格参数 |

| 输入 | 说明 |
| --- | --- |
| 商品图 | 1 张 |
| 商品类目 | `服饰` `箱包` `鞋品` `婴童用品` `宠物` `家居用品` `美妆` `3C数码` `电器` `家具灯饰` `食品厨具` `珠宝饰品` `工业和农业` `其他` |
| 商品信息 | 100 字内的卖点，例：`商品卖点：舒适透气、柔软亲肤，适合入群：1个月-2岁宝宝` |

**不做**：不编造商品没有的功能与认证；不生成虚假促销信息与虚假对比数据；不改商品的款式与颜色。

---

## 2、输入素材规则

生成前先自检这几条硬性约束：

- 大小：**20KB ~ 15MB**
- 分辨率：**大于 400×400**
- 格式：**jpg / jpeg / png / webp**

**输入建议**

| 做法 | 说明 |
| --- | --- |
| ✅ 商品图干净 | 带文字的图会让新排版和旧文字打架，先走 [remove-watermark](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/remove-watermark/skill.md) |
| ✅ 卖点写成短句 | `亲肤不扎` 比 `采用优质柔软亲肤面料不刺激皮肤` 好排版 |
| ✅ 文案字数控制 | 主标题 ≤ 8 字，副标题 ≤ 16 字，图标文案 ≤ 5 字 |
| ❌ 一个模块塞十条卖点 | 排不下，会挤成乱码 |
| ❌ 未经核实的功效宣称 | 合规风险 |

---

## 3、中文排版能出对的四个条件

中文详情页最大的失败模式是**乱码**。这四条同时满足才稳定：

| 条件 | 写法 |
| --- | --- |
| 1 · 把每一段文案原文写进 prompt | 用「」括起来：`大字「粗棒麻花 复古落肩」`、`小字「羊毛混纺 · 加厚保暖 · 男女同款」` |
| 2 · 指定字体族与字重 | `字体为无衬线黑体，字号层级分明` |
| 3 · 指定版式关系 | `右上方留白区排版中文标题`、`底部一行三个圆形图标配文字` |
| 4 · 显式要求不乱码 | `中文字必须清晰正确无乱码` |

**文案越短越不容易崩**。四字、六字标题的成功率远高于长句。

**分块生成比一次生成整页更稳**：首屏 banner 一条 prompt，卖点块一条，材质块一条，最后用设计工具拼成长图。一次生成整页时，越往下的模块文字越容易崩。

---

## 4、类目 → 版式基调

| 类目 | 背景与色调 | 文案侧重 |
| --- | --- | --- |
| 服饰 | 米灰 / 燕麦色，暖调，大留白 | 版型、面料、场合 |
| 箱包 / 鞋品 | 深灰渐变，硬光，质感优先 | 材质、容量、耐用 |
| 婴童 / 宠物 | 浅粉 / 奶油色，柔光 | 安全、亲肤、适用月龄 |
| 美妆 | 大理石 / 丝绒，高对比 | 成分、质地、效果 |
| 3C / 电器 | 深色科技风，冷光 | 参数、性能、接口 |
| 家居 / 家具灯饰 | 实景房间，自然光 | 尺寸、材质、搭配 |
| 食品厨具 | 木质餐桌，暖光 | 原料、工艺、口感 |
| 珠宝饰品 | 深色绒布，聚光 | 材质、克重、工艺 |

---

## 5、工具调用

本技能使用 dLazy 的 **`seedream-5.0-pro`**（专业档图像模型，在**中文字形与复杂排版**上明显强于同类；本技能的产出带大量中文文案，字形正确率是首要指标）。

### 调用方式

两种等价写法，选一种。统一入口会自动选后端、失败重试、建目录落盘、估算成本：

```bash
# A. 统一入口（推荐）：可切任意后端，加 --dry-run 不计费空跑
node scripts/gen.mjs

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "item-detail",
  "version": "1.0.19",
  "publishedAt": 1791597100654
}

references/model-flags.md

# `seedream-5.0-pro` 参数清单

本技能默认用的模型的完整参数。日常只需要「参数约定」里那几个，
这份清单在需要用到非常规参数时再看。

**CRITICAL INSTRUCTION FOR AGENT**:
Run the `dlazy seedream-5.0-pro` command to get results.

```bash
dlazy seedream-5.0-pro -h

Options:
  --prompt <prompt>          Prompt
  --images [images...]       Images [image: url or local path] (max 10)
  --resolution <resolution>  Resolution [default: 2k] (choices: "2k")
  --size <size>              Size [default: 16:9] (choices: "1:1", "4:3",
                             "3:4", "16:9", "9:16", "3:2", "2:3", "21:9")
  --dry-run                  Print payload without executing the tool
  --no-wait                  Return generateId immediately for async tasks
  --timeout <seconds>        Max seconds to wait for async completion (default:
                             "1800")
  --input <jsonOrFile>       Inline JSON or @path/to/file.json — merged under
                             flag values (flags win)
  --save <path>              Download the result asset to this local path
                             (mkdir + retry handled for you). A destination
                             path — NOT a response format; for stdout shape use
                             --format
  --batch <n>                Fan-out N parallel runs (cloud tools only)
                             (default: "1")
  -h, --help                 display help for command
```

> Any flag also accepts pipe references — `-` (auto-pick from upstream stdin), `@N` (n-th output), `@N.path` (jsonpath into output), `@*` (all primary values), `@stdin` / `@stdin:path` (whole envelope). See `dlazy --help` for details.

---

换其他后端时参数由 `scripts/gen.mjs` 统一翻译，见 [`provider-cli.md`](provider-cli.md)。

references/provider-cli.md

<!-- 由 scripts/build-skills.mjs 从 shared/references/provider-cli.md 同步生成，不要直接改这里。 -->
# 后端调用参考

技能正文只写「要生成什么」。认证、计费、错误码、输出结构这些每个技能都一样的东西放在这里，
**用到时再读**，不占技能的常驻上下文。

---

## 一、认证

### 默认后端 dLazy

```bash
dlazy login            # 设备码流程，远程 shell 也能用，自动写入本地配置
dlazy auth set <KEY>   # 已有 key 时直接写入
```

key 存在用户配置目录（macOS/Linux `~/.dlazy/config.json`，Windows `%USERPROFILE%\.dlazy\config.json`），
权限限本机用户。也可以每次调用用环境变量 `DLAZY_API_KEY` 传入。

手动获取：登录 [dlazy.com](https://dlazy.com) → [API Key 页面](https://dlazy.com/dashboard/organization/api-key)。
key 按组织隔离，可随时轮换或吊销。

### 其他后端

本技能库不锁定单一厂商。配好任意一家的 key 即可跑：

| 后端 | 环境变量 | 说明 |
| --- | --- | --- |
| `dlazy` | `dlazy login` 或 `DLAZY_API_KEY` | 默认，最省事 |
| `openai` | `OPENAI_API_KEY` | 走 `/v1/images/edits` 与 `/v1/images/generations` |
| `gemini` | `GEMINI_API_KEY` | Nano Banana 系列 |
| `fal` | `FAL_KEY` | |
| `replicate` | `REPLICATE_API_TOKEN` | |
| `ark` | `ARK_API_KEY` + `ARK_MODEL` | 火山方舟，模型 ID 需按开通情况填 |

选路优先级：`--provider` 参数 > `PROVIDER` 环境变量 > 第一个配了 key 的 > `dlazy`。

```bash
node scripts/gen.mjs --doctor     # 看当前哪个后端可用
```

各后端的模型 ID 可用 `GEN_MODEL_OPENAI` / `GEN_MODEL_GEMINI` / `GEN_MODEL_FAL` /
`GEN_MODEL_REPLICATE` / `GEN_MODEL_ARK` 覆盖。**厂商目录会变，以各家最新文档为准。**

---

## 二、两种调用方式

### 方式 A：统一入口（推荐）

```bash
node scripts/gen.mjs --task <技能名> --prompt '...' --images a.jpg b.jpg --save out.jpg
```

它负责：后端选路、默认尺寸档位、失败重试（429/5xx 指数退避）、落盘建目录、成本估算。

```bash
node scripts/gen.mjs --task flat-lay --prompt '...' --dry-run   # 不调用不计费，只看要发什么
node scripts/gen.mjs --help
```

### 方式 B：直接用 dLazy CLI

不想引入 Node 依赖时，技能正文里的 `dlazy ...` 命令可以原样执行，效果等价。

```bash
npx @dlazy/[email protected] <command>     # 不装全局二进制
```

- CLI 源码：[github.com/dlazy-ai/cli](https://github.com/dlazy-ai/cli) · npm 包 `@dlazy/cli`

---

## 三、数据流向

调用 dLazy 时：提示词与参数发往 `api.dlazy.com`；传入的本地图片会上传到 `files.dlazy.com`
供模型读取；产出 URL 同样托管在 `files.dlazy.com`。这是云端生成 API 的通用形态。
换成其他后端时，数据流向对应厂商，不经过 dLazy。

---

## 四、输出结构

`gen.mjs`（加 `--json`）：

```json
{
  "ok": true,
  "task": "flat-lay",
  "provider": "dlazy",
  "model": "gpt-image-2",
  "files": ["docs/flat-lay/output-sku001.jpg"],
  "texts": [],
  "estimatedCredits": 60,
  "elapsedMs": 58213
}
```

dLazy CLI 原生：

```json
{
  "ok": true,
  "result": {
    "tool": "gpt-image-2",
    "data": { "urls": ["https://files.dlazy.com/data/ai/....jpg"] },
    "savedPath": "docs/flat-lay/example-output.jpg"
  }
}
```

加 `--no-wait` 的异步任务不返回 `data`，返回 `task: { generateId, status }`，
用 `dlazy status <generateId> --wait` 轮询。

文本类模型（如质检）产出在 `result.data.texts[0]`：

```bash
dlazy claude-sonnet-5 --prompt '...' --images x.jpg \
  | python3 -c 'import sys,json;print(json.load(sys.stdin)["result"]["data"]["texts"][0])'
```

---

## 五、错误处理

| Code | 类型 | 示例 |
| --- | --- | --- |
| 401 | 未授权 / 无 key | `ok: false, code: "unauthorized"` |
| 501 | 缺必填参数 | `error: required option '--prompt <prompt>' not specified` |
| 502 | 本地文件读不到 | `Error: Image file not found: ...` |
| 503 | 余额不足 | `ok: false, code: "insufficient_balance"` |
| 503 | 服务端错误 | `HTT

scripts/lib/tasks.json

{
  "_note": "技能 → 默认模型与参数。dlazy 列为默认后端的模型名；其他后端走 providers.mjs 的通用映射，可用 GEN_MODEL_<PROVIDER> 覆盖。",
  "_credits": { "gpt-image-2": 60, "seedream-5.0": 30, "seedream-5.0-pro": 45, "banana-pro": 25, "claude-sonnet-5": 3 },
  "tasks": {
    "flat-lay":                { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "wear-everything":         { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "image-fusion":            { "model": "seedream-5.0",     "size": "3:4",       "resolution": "2k" },
    "one-shot":                { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "fission-pattern":         { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "item-detail":             { "model": "seedream-5.0-pro", "size": "3:4",       "resolution": "2k" },
    "creative-scene":          { "model": "banana-pro",       "size": "1024x1536", "format": "jpeg" },
    "batch-image":             { "model": "seedream-5.0",     "size": "3:4",       "resolution": "2k" },
    "to-3d":                   { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "clothing-extraction":     { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "fabric-on-body":          { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "clothing-detail":         { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "clothing-grass-planting": { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "item-selling-point":      { "model": "seedream-5.0-pro", "size": "1:1",       "resolution": "2k" },
    "item-change-background":  { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "remove-watermark":        { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "material-enhancement":    { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "item-repair":             { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "detect-task":             { "model": "claude-sonnet-5",  "text": true },
    "listing-optimizer":       { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "cross-border-localize":   { "model": "seedream-5.0-pro", "size": "1:1",       "resolution": "2k" },
    "brand-kit":               { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "platform-compliance":     { "model": "claude-sonnet-5",  "text": true },
    "main-image-video":        { "model": "$DLAZY_VIDEO_MODEL", "video": true },
    "product-

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Machine-readable data

The same record, as JSON, for agents and crawlers.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/dlazyai/skills/item-detail",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/item-detail",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:37:07.135Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
      "value": "OpenClaw",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-detail/contract",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-detail/contract",
      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:37:07.135Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "1.3K downloads",
      "href": "https://clawhub.ai/dlazyai/item-detail",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-detail",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:37:07.135Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "1.0.19",
      "href": "https://clawhub.ai/dlazyai/item-detail",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-detail",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:51:40.654Z",
      "isPublic": true
    },
    {
      "factKey": "handshake_status",
      "category": "security",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-detail/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-detail/trust",
      "sourceType": "trust",
      "confidence": "medium",
      "observedAt": null,
      "isPublic": true
    }
  ],
  "events": [
    {
      "eventType": "release",
      "title": "Release 1.0.19",
      "description": "例行版本更新 2026-10-10",
      "href": "https://clawhub.ai/dlazyai/item-detail",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-detail",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:51:40.654Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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