Xpersona
agentCLAWHUBUnverified

一键替换服装面料 Fabric on Body

一键替换服装面料。版式图 + 面料图 → 换上新面料的样衣图，垂坠与光泽随材质变。当用户说「换面料」「换材质」「试布料」「面料上身」「同款不同料」时使用。 Skill: 一键替换服装面料 Fabric on Body Owner: dlazyai Summary: 一键替换服装面料。版式图 + 面料图 → 换上新面料的样衣图，垂坠与光泽随材质变。当用户说「换面料」「换材质」「试布料」「面料上身」「同款不同料」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:49:39.917Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:42:20.407Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:42:31.563Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:15:32.141Z | user 例行版本更新 2026-10-02 v1.0.15 |

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:fabric-on-body
  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-fabric-on-body/snapshot"

Documentation

CLAWHUB

146,719 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: fabric-on-body
version: 1.0.19
description: 一键替换服装面料。版式图 + 面料图 → 换上新面料的样衣图，垂坠与光泽随材质变。当用户说「换面料」「换材质」「试布料」「面料上身」「同款不同料」时使用。
---

# fabric-on-body — 一键替换服装面料

**版型不动，面料换掉**。给一张服装版式图 + 一张面料小样，输出这个版型用新面料做出来的样衣效果图。

价值在打样前：一个版型试 8 种面料，传统做法是打 8 件样衣（每件几天、几百块）；这里是 8 次生成。

---

## 生成效果示例

| 输入：服装版式图 | 输入：面料图 |
| --- | --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/fabric-on-body/style-sheet.jpg" width="260"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/fabric-on-body/fabric-swatch.jpg" width="260"> |
| `style-sheet.jpg` — 落肩宽松圆领毛衣版型，800×800 | `fabric-swatch.jpg` — 象牙白真丝缎面小样，640×640 |

实际执行的命令：

```bash
dlazy gpt-image-2 \
  --prompt 'Fabric replacement for a garment style sheet. Image 1 is the garment pattern/style reference: an oversized drop-shoulder crewneck sweater with ribbed collar, cuffs and hem. Image 2 is the target fabric: ivory silk satin with a soft lustrous sheen and fine weave. Re-render the exact same garment silhouette from image 1 in the fabric from image 2. Keep the pattern identical: same oversized drop-shoulder cut, same body length, same sleeve length, same collar/cuff/hem construction, same flat-lay layout and camera angle. Replace only the material — the sweater must now read as ivory silk satin with specular highlights on the folds and soft drape instead of chunky knit. Clean white background, even studio light, no text.' \
  --images docs/fabric-on-body/style-sheet.jpg docs/fabric-on-body/fabric-swatch.jpg \
  --size 1024x1024 --quality medium --imageFormat jpeg \
  --save docs/fabric-on-body/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1024。落肩剪裁、身长袖长、罗纹领口袖口下摆的结构与平铺角度都与版式图一致；材质从粗针织变成象牙白真丝缎面，褶皱上出现高光、垂坠变得柔顺，罗纹部位仍以缎面质感保留。

---

## 1、能力边界

| 输入 | 说明 |
| --- | --- |
| 服装版式图 | 决定版型、剪裁、结构与拍摄角度 |
| 面料图 | 决定材质、纹理、光泽与垂坠感（面料小样特写最佳） |
| 服装类型 | 帮助模型理解结构（上装 / 下装 / 连衣裙 / 外套 / 内衣） |

| 保持不变 | 会改变 |
| --- | --- |
| 版型与剪裁、身长袖长、领口/袖口/下摆结构、缝线位置、拍摄角度与构图 | 材质与织法、表面光泽、垂坠与褶皱形态、厚度观感 |

**不做**：不改版型比例；不改变服装的结构设计；不承诺真实打样的手感与克重——输出是视觉预览，不是工艺样衣。

---

## 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**

**版式图（✅）**：纯色背景、结构清晰、正面完整的平铺图或人台图。

**面料图（✅）**：面料小样特写，能看清织纹与光泽；有褶皱转折更好（能交代垂坠）。

**会明显拉低效果的输入（❌）**

| 问题 | 说明 |
| --- | --- |
| 面料图拍太远 | 看不清织纹，只会当成一块纯色 |
| 面料图带强环境色 | 黄光下拍的白布会换成米黄色 |
| 版式图有复杂印花 | 原印花会和新面料打架，先用 [item-repair](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/item-repair/skill.md) 或换素图 |
| 面料与品类不匹配 | 硬挺牛仔布做不出吊带裙的垂坠，模型会硬凑 |

---

## 3、面料替换的关键：写清「材质带来的物理变化」

只说「换成真丝」不够——模型不知道该怎么改光影。要把材质翻译成**可画出来的物理特征**：

| 面料 | 要写的物理特征 |
| --- | --- |
| 真丝 / 缎面 | `lustrous specular highlights along the folds, fluid drape, soft continuous gradients` |
| 粗针织 | `chunky knit loops with visible yarn twist, matte fibre halo, structured heavy drape` |
| 牛仔 | `twill diagonal weave, sli

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "fabric-on-body",
  "version": "1.0.19",
  "publishedAt": 1791596979917
}

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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