Xpersona
agentCLAWHUBUnverified

商品图精修 Item Repair

商品精修、去褶皱。随手拍的商品图 → 可直接上架的精修图。当用户说「精修」「去褶皱」「修图」「拍得不好看」「整理一下」时使用。 Skill: 商品图精修 Item Repair Owner: dlazyai Summary: 商品精修、去褶皱。随手拍的商品图 → 可直接上架的精修图。当用户说「精修」「去褶皱」「修图」「拍得不好看」「整理一下」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:52:09.773Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:44:34.406Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:44:34.062Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:17:03.280Z | user 例行版本更新 2026-10-02 v1.0.15 | 2026-09-30T01:48:37.

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-repair
  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-repair/snapshot"

Documentation

CLAWHUB

146,699 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: item-repair
version: 1.0.19
description: 商品精修、去褶皱。随手拍的商品图 → 可直接上架的精修图。当用户说「精修」「去褶皱」「修图」「拍得不好看」「整理一下」时使用。
---

# item-repair — 一键提升商品图质感

把**随手拍的商品图**修成**可上架的精修图**：压平褶皱、摆正对称、匀光、提纯背景。

和 [material-enhancement](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/material-enhancement/skill.md) 的分工：material-enhancement 修**面料纹理**（在模特图上），本技能修**摆放与光照**（在商品图上）。两者可以串起来用。

---

## 生成效果示例

| 输入：原商品图 |
| --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/item-repair/source-flatlay.jpg" width="280"> |
| `source-flatlay.jpg` — 军绿麻花针织毛衣平铺图（袖身有随机褶皱、左右不完全对称），800×800 |

实际执行的命令（平铺图精修 + 材质增强）：

```bash
dlazy gpt-image-2 \
  --prompt 'Studio retouch of a flat-lay garment photo. Clean up this olive-green cable-knit sweater to catalog standard: press out the random wrinkles and creases in the body and sleeves, straighten and symmetrise the silhouette, square the shoulders, align both sleeves evenly, tidy the collar and hem, and even out the lighting so there is no hot spot or colour cast. Keep the cable-knit and diamond stitch pattern, the exact olive-green colour, the ribbed collar/cuffs/hem and the woven cuff label unchanged and sharper than before. Pure white seamless background with a subtle soft contact shadow. Photorealistic, print-ready, no text, no watermark.' \
  --images docs/item-repair/source-flatlay.jpg \
  --size 1024x1024 --quality high --imageFormat jpeg \
  --save docs/item-repair/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1024，60 credits。随机褶皱被压平、肩线方正、两只袖子长度与角度对齐、下摆罗纹平整、光照均匀无高光斑；麻花与菱形织法、军绿色、罗纹结构与右袖织标保留且比原图更清晰，背景提纯为白底带柔和接地投影。

---

## 1、能力边界

| 模板 | 做什么 |
| --- | --- |
| 平铺图精修 | 摊平、左右对称、方正肩线、袖长对齐、去褶皱、匀光、纯净背景 |
| 服装去皱 | 只压平随机褶皱与折痕，保留结构性褶（褶裥、抽绳、垂坠） |
| 通用精修 | 去画面杂物、匀光、提纯背景、提升清晰度（非服装类目也适用） |
| 自定义 | 自己描述要修什么 |

| 附加 | 说明 |
| --- | --- |
| 多图输入 | 同一商品 1-4 张，模型综合多角度信息理解结构 |
| 材质增强 | 开关；开启后表面纹理更清晰（等价于 `--quality high`） |

**不做**：不改款式、颜色、图案、五金与结构；不压平结构性褶皱（褶裥、抽绳、荷叶边）；不用于把次品图修成正品图。

---

## 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 |
| --- | --- |
| ✅ 同一商品多角度 | 1-4 张，正面 + 背面 + 细节，模型能更准地推断结构 |
| ✅ 商品完整入画 | 出画部分只能靠编 |
| ✅ 光线不要太杂 | 混合色温的光很难匀 |
| ❌ 商品有明显破损/污渍 | 修图不该掩盖商品缺陷 |
| ❌ 多个不同商品同框 | 一次只修一个商品 |

---

## 3、四个模板的 prompt 写法

| 模板 | prompt 主体 |
| --- | --- |
| 平铺图精修 | `Press out the random wrinkles and creases, straighten and symmetrise the silhouette, square the shoulders, align both sleeves evenly, tidy the collar and hem, and even out the lighting so there is no hot spot or colour cast.` |
| 服装去皱 | `Remove only the random wrinkles and packing creases. Preserve every structural fold — pleats, gathers, drawstring ruching and intentional drape must stay exactly as they are.` |
| 通用精修 | `Remove stray objects, dust and reflections from the frame, even out the lighting, purify the background t

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "item-repair",
  "version": "1.0.19",
  "publishedAt": 1791597129773
}

references/model-flags.md

# `gpt-image-2` 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 gpt-image-2` command to get results.

```bash
dlazy gpt-image-2 -h

Options:
  --prompt <prompt>            Prompt
  --images [images...]         Images [image: url or local path] (max 5)
  --size <size>                Size [default: auto] (choices: "1024x1024",
                               "1536x1024", "1024x1536", "2048x2048",
                               "2048x1152", "3840x2160", "2160x3840", "auto")
  --imageFormat <imageFormat>  Image Format [default: jpeg] (choices: "jpeg",
                               "png", "webp")
  --quality <quality>          Quality [default: medium] (choices: "low",
                               "medium", "high")
  --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.

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}

Record generated Oct 10, 2026.

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