Xpersona
agentCLAWHUBUnverified

卖点主图生成 Item Selling Point

商品图生成带文案排版的转化主图。商品图 + 卖点 → 带中文文案的电商主图。当用户说「主图」「加卖点文案」「转化图」「促销图」「主图文案」时使用。 Skill: 卖点主图生成 Item Selling Point Owner: dlazyai Summary: 商品图生成带文案排版的转化主图。商品图 + 卖点 → 带中文文案的电商主图。当用户说「主图」「加卖点文案」「转化图」「促销图」「主图文案」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:52:46.587Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:44:52.649Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:44:52.636Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:17:18.125Z | user 例行版本更新 2026-10-02 v1.0.15 | 20

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-selling-point
  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-selling-point/snapshot"

Documentation

CLAWHUB

146,959 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: item-selling-point
version: 1.0.19
description: 商品图生成带文案排版的转化主图。商品图 + 卖点 → 带中文文案的电商主图。当用户说「主图」「加卖点文案」「转化图」「促销图」「主图文案」时使用。
---

# item-selling-point — 商品图生成电商主图

商品图 + 卖点文案 → **带排版的转化型电商主图**。

和 [item-detail](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/item-detail/skill.md) 的分工：item-detail 出**详情页长图的各个模块**（信息量大、多模块），本技能出**主图位的单张方图**（信息密度低、要在缩略图尺寸下也能看清）。

---

## 生成效果示例

| 输入：商品图 |
| --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/item-selling-point/product-shoes.jpg" width="280"> |
| `product-shoes.jpg` — 黑色鳄鱼纹亮面皮革布洛克德比鞋，白底，800×800 |

实际执行的命令：

```bash
dlazy seedream-5.0-pro \
  --prompt '电商正方形主图。主体是图1 中的黑色亮面皮革布洛克德比鞋，鞋型、雕花孔、鞋带与厚底必须与图1完全一致，商品去底后放在画面中央偏左，下方带柔和投影。背景为深灰到浅灰渐变，右侧竖排中文卖点文案：主标题大字「真皮软底 通勤久站不累」，下方两行小字卖点「牛皮鞋面 · 防滑厚底」「三防涂层 · 雨天不怕」。左上角一枚红色圆形促销角标写「满300减30」。无衬线黑体，字号层级分明，排版整齐，中文字清晰正确无乱码，商业电商主图设计。' \
  --images docs/item-selling-point/product-shoes.jpg --size 1:1 \
  --save docs/item-selling-point/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1:1 / 2048×2048，5 credits。鞋的鳄鱼纹压花、雕花孔、鞋带与厚齿底保持一致，去底后居中偏左带柔和投影；右侧主标题「真皮软底 通勤久站不累」与两行小字卖点字形正确、层级分明；左上红色圆形角标「满300减30」；深灰渐变背景。

---

## 1、能力边界

| 输入 | 说明 |
| --- | --- |
| 商品图 | 1 张 |
| 去除商品图背景 | 开关；开启后商品去底再合成 |
| 商品类目 | 决定背景与配色基调 |
| 参考图 | 可选，决定版式与配色 |
| **商品功能点** | ≤100 字，多个用 `/` 分隔，例：`持久续航/防水防汗` |
| **营销利益点** | ≤100 字，多个用 `/` 分隔，例：`年货狂欢节/满300-30` |

**功能点和利益点要分开**：功能点是产品能力（排在主标题/副标题），利益点是促销信息（排在角标/腰带）。混在一起排版会乱。

**不做**：不编造商品没有的功能；不生成虚假促销（不存在的活动、虚假折扣、虚假原价）；不使用绝对化用语（最、第一、国家级）；不改商品外形与颜色。

---

## 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 |
| --- | --- |
| ✅ 白底或纯色底商品图 | 去底最干净 |
| ✅ 文案精简 | 主图在列表页只有 200px 左右，长句根本看不见 |
| ✅ 主标题 ≤10 字，小字 ≤12 字/行，角标 ≤8 字 | 缩略图可读性的经验值 |
| ❌ 塞五条卖点 | 主图最多承载 1 个主标题 + 2 行小字 + 1 个角标 |
| ❌ 未经核实的功效/促销 | 平台合规风险 |

---

## 3、主图版式的三个区

把方图切成三个区，每个区只放一种信息：

```text
┌─────────────────────────┐
│ ⓐ 角标区（左上/右上）      │  ← 营销利益点：「满300减30」
│                         │
│      ⓑ 商品区（中央偏左）  │  ← 商品去底 + 投影
│                         │
│              ⓒ 文案区（右侧）│  ← 功能点：主标题 + 2 行小字
└─────────────────────────┘
```

写进 prompt：

```text
商品去底后放在画面中央偏左，下方带柔和投影。
右侧竖排中文卖点文案：主标题大字「[≤10字]」，下方两行小字卖点「[≤12字]」「[≤12字]」。
左上角一枚[形状]促销角标写「[≤8字]」。
```

**缩略图检查**：把输出缩到 200×200 看一眼——主标题还认得出来吗？认不出就再缩短文案或加大字号层级。

---

## 4、类目 → 背景与配色

| 类目 | 背景 | 文字色 | 角标色 |
| --- | --- | --- | --- |
| 服饰 | 米灰 / 燕麦色渐变 | 深灰 | 砖红 |
| 箱包 / 鞋品 | 深灰到浅灰渐变 | 黑 | 正红 |
| 婴童 / 宠物 | 奶油 / 浅粉 | 暖棕 | 珊瑚粉 |
| 美妆 | 大理石 / 丝绒 | 黑金 | 酒红 |
| 3C / 电器 | 深色科技渐变 | 白 | 荧光蓝 |
| 家居 / 家具灯饰 | 实景房间浅景深 | 深棕 | 橄榄绿 |
| 食品厨具 | 木质台面暖调 | 深棕 | 橙红 |
| 珠宝饰品 | 深色绒布聚光 | 白金 | 深红 |

---

## 5、工具调用

本技能使用 dLazy 的 **`seedream-5.0-pro`**（专业档图像模型，中文字形与版面控制最稳；主图上的文案必须一眼可读、零乱码，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 --task item-

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "item-selling-point",
  "version": "1.0.19",
  "publishedAt": 1791597166587
}

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-selling-point",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/item-selling-point",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:55:33.510Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
      "value": "OpenClaw",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-selling-point/contract",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-selling-point/contract",
      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:55:33.510Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "1.3K downloads",
      "href": "https://clawhub.ai/dlazyai/item-selling-point",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-selling-point",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:55:33.510Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "1.0.19",
      "href": "https://clawhub.ai/dlazyai/item-selling-point",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-selling-point",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:52:46.587Z",
      "isPublic": true
    },
    {
      "factKey": "handshake_status",
      "category": "security",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-selling-point/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-item-selling-point/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-selling-point",
      "sourceUrl": "https://clawhub.ai/dlazyai/item-selling-point",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:52:46.587Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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