Xpersona
agentCLAWHUBUnverified

服装细节放大图 Clothing Detail

服装工艺细节放大图。服装图 → 面料纹理、走线、织法的微距特写。当用户说「细节图」「特写」「面料放大」「工艺展示」「近景细节」时使用。 Skill: 服装细节放大图 Clothing Detail Owner: dlazyai Summary: 服装工艺细节放大图。服装图 → 面料纹理、走线、织法的微距特写。当用户说「细节图」「特写」「面料放大」「工艺展示」「近景细节」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:46:57.263Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:39:56.956Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:40:27.426Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:14:00.107Z | user 例行版本更新 2026-10-02 v1.0.15 | 2026-09-30

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.2k

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. 1.2K 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
1.2K 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:clothing-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-clothing-detail/snapshot"

Documentation

CLAWHUB

146,800 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: clothing-detail
version: 1.0.19
description: 服装工艺细节放大图。服装图 → 面料纹理、走线、织法的微距特写。当用户说「细节图」「特写」「面料放大」「工艺展示」「近景细节」时使用。
---

# clothing-detail — 服装图生成细节放大图

一张服装图 → **局部微距特写**。详情页里「证明这件衣服做得好」的那几张图。

为什么需要：转化率高的详情页通常有 2~3 张细节图（领口、袖口、面料纹理），但拍微距要专门的镜头和布光。本技能从常规商品图推出这些特写。

---

## 生成效果示例

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

实际执行的命令：

```bash
dlazy gpt-image-2 \
  --prompt 'Macro detail shot for an e-commerce detail page. Zoom into the shoulder-and-collar area of this olive-green cable-knit sweater and render a photorealistic close-up that fills the frame. Show the ribbed crewneck collar meeting the raglan-style cable panel, individual yarn plies and the twist of the cable braid, the loft of the wool fibres, and soft directional light raking across the surface to reveal depth. Keep the colour and stitch pattern exactly as in the source. Shallow depth of field with the far edge softly out of focus, clean neutral background bokeh, no person, no text, no watermark.' \
  --images docs/clothing-detail/garment-flatlay.jpg \
  --size 1024x1024 --quality high --imageFormat jpeg \
  --save docs/clothing-detail/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1024，60 credits。领口罗纹与麻花panel的交接、每根纱线的捻向、羊毛纤维的绒毛感都被解析出来，侧光让菱形提花的凹凸立体可见，远端落入柔和虚化。

---

## 1、能力边界

| 能力 | 说明 |
| --- | --- |
| 取景部位 | 领口罗纹 / 袖口 / 下摆 / 纽扣 / 拉链 / 口袋 / 刺绣 / 印花 / 织法结构 / 面料纤维 |
| 风格控制 | 参考图（照抄某张细节图的机位与光线）或自定义提示词 |
| 服装类型 | 帮助模型判断哪些部位值得放大 |
| 生成比例 | `1:1`（方图细节位）/ `3:4`（竖版详情页） |

**不做**：不改颜色、织法与结构；不添加原图没有的工艺（不存在的刺绣、不存在的拉链）；不虚构面料成分。

---

## 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 |
| --- | --- |
| ✅ 原图分辨率越高越好 | 微距是在放大原图信息，原图糊 = 细节图编 |
| ✅ 目标部位在原图里清晰可见 | 原图里看不清的部位，输出的是模型的想象 |
| ✅ 一次只放大一个部位 | 一张图里塞三个特写等于都不清楚 |
| ❌ 低分辨率 / 强压缩图 | 会放大出塑料感的假纹理 |
| ❌ 目标部位被遮挡 | 挡住的工艺只能靠编 |

---

## 3、取景部位 → prompt 写法

| 部位 | 取景描述 |
| --- | --- |
| 领口罗纹 | `the ribbed crewneck collar meeting the body panel, showing rib wale spacing and the seam join` |
| 袖口 | `the ribbed cuff and the sleeve seam, showing rib elasticity and stitch density` |
| 下摆 | `the hem band and side seam, showing hem width and the finishing stitch` |
| 纽扣 | `a single button and its buttonhole, showing button material, thread cross and hole finishing` |
| 拉链 | `the zipper teeth and puller, showing tooth pitch, metal finish and the tape stitching` |
| 刺绣 / 印花 | `the [刺绣/印花] motif filling the frame, showing thread direction / print edge sharpness and substrate texture` |
| 织法结构 | `the [麻花/罗纹/提花] stitch structure, showing individual yarn plies and the twist of each loop` |
| 面料纤维 | `the fabric surface at extreme magnification, showing fibre halo and weave interlacing` |

*

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "clothing-detail",
  "version": "1.0.19",
  "publishedAt": 1791596817263
}

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.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/dlazyai/skills/clothing-detail",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/clothing-detail",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T22:08:38.348Z",
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      "sourceUrl": "https://clawhub.ai/dlazyai/clothing-detail",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T22:08:38.348Z",
      "isPublic": true
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  "events": [
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      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:46:57.263Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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