Xpersona
agentCLAWHUBUnverified

鞋包配饰真人穿戴 Wear Everything

鞋包配饰真人穿戴图。商品图 + 模特参考图 → 真人佩戴图，落位、透视与阴影自然。当用户说「鞋包上脚」「配饰上身」「墨镜戴上」「首饰佩戴图」「包包上身」时使用。 Skill: 鞋包配饰真人穿戴 Wear Everything Owner: dlazyai Summary: 鞋包配饰真人穿戴图。商品图 + 模特参考图 → 真人佩戴图，落位、透视与阴影自然。当用户说「鞋包上脚」「配饰上身」「墨镜戴上」「首饰佩戴图」「包包上身」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:58:16.209Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:48:58.741Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:48:24.739Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:19:49.585Z | user 例行版本更新 2026-10-02 v1.0.1

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:wear-everything
  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-wear-everything/snapshot"

Documentation

CLAWHUB

146,684 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: wear-everything
version: 1.0.19
description: 鞋包配饰真人穿戴图。商品图 + 模特参考图 → 真人佩戴图，落位、透视与阴影自然。当用户说「鞋包上脚」「配饰上身」「墨镜戴上」「首饰佩戴图」「包包上身」时使用。
---

# wear-everything — 鞋包配饰一键真人穿戴

把一张**鞋 / 包 / 手表 / 眼镜 / 帽子 / 围巾 / 项链 / 耳饰**的商品图，变成**真人正确佩戴**的商拍图。

和 [flat-lay](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/flat-lay/skill.md) 的区别：flat-lay 处理「穿在身上的衣服」，本技能处理「戴在身上的东西」——商品只占画面的一小块区域，因此**选区（商品出现在参考图的哪个位置）**是成败关键。

---

## 生成效果示例

| 输入：商品图 | 输入：参考图 |
| --- | --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/wear-everything/product-sunglasses.jpg" width="280"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/wear-everything/model-reference.jpg" width="280"> |
| `product-sunglasses.jpg` — 玳瑁色方框墨镜，768×1024 | `model-reference.jpg` — 女青年正脸、夜景街拍，768×1024 |

实际执行的命令：

```bash
dlazy gpt-image-2 \
  --prompt 'On-model accessory product photography. Image 1 is the product: a pair of tortoise-brown rectangular sunglasses with dark grey lenses. Image 2 is the model/scene reference. Put the sunglasses from image 1 onto the face of the model in image 2, correctly seated on the nose bridge and ears with natural perspective, realistic lens reflections and a soft shadow on the cheekbones. Keep the product 100% faithful: identical frame shape, tortoise-brown acetate color and grain, hinge and temple design, lens tint. Change nothing else — face, hair, scarf, coat, bag chain, night street background, colour grading and crop must stay pixel-identical to image 2. Photorealistic, no text, no watermark.' \
  --images docs/wear-everything/product-sunglasses.jpg docs/wear-everything/model-reference.jpg \
  --size 1024x1536 --quality medium --imageFormat jpeg \
  --save docs/wear-everything/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1536。墨镜按正确的鼻梁/耳挂关系落位，镜框玳瑁纹理与镜片色保留；模特五官、围巾千鸟格、夜景街道与色调保持原样。

---

## 1、能力边界

| 能力 | 说明 |
| --- | --- |
| 单视角图 | 上传 1 张商品图（正面或主视角） |
| 多视角图 | 上传同一商品的多个角度，帮助模型理解立体结构（鞋侧面+鞋底、包正面+内里） |
| 参考图 | 提供真人模特、姿势、场景与光线 |
| 选区 | 在参考图上框出商品该出现的位置（眼部 / 手腕 / 颈部 / 脚部 / 肩背 / 头顶） |
| 支持品类 | 鞋、包、手表、眼镜墨镜、帽子、围巾、项链、耳饰、腰带、手套等 |

**不做**：不改商品外形、颜色、五金、logo 与镜片色；不处理服装（用 [flat-lay](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/flat-lay/skill.md)）；不用于伪造他人肖像代言。

---

## 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**

**推荐的输入类型（✅）**

| 类型 | 说明 |
| --- | --- |
| 时尚皮鞋 | 单只或一双完整入画，主视角 |
| 优雅手表 | 表盘正面清晰，表带完整 |
| 珍珠项链 | 摊开或悬挂，链身完整 |
| 渔夫帽 | 帽型完整，纹理清晰 |
| 防晒墨镜 | 镜框展开，镜片颜色真实 |

**会明显拉低效果的输入（❌）**

| 问题 | 说明 |
| --- | --- |
| 商品不完整 | 只拍到一半的鞋、被裁掉链扣的项链 |
| 挂件过多 | 包上挂满吊饰、丝巾，模型分不清主体 |
| 商品不清晰 | 模糊、过曝、金属反光糊成一片 |

---

## 3、选区：本技能最重要的参数

原站在参考图上直接框选，本技能改用 **prompt 显式指定解剖位置 + 佩戴姿态**，等价且更可控：

| 品类 | 选区描述（写进 prompt） |
| --- | --- |
| 眼镜 / 墨镜 | `seated on the nose bridge and hooked over both ears, temples visible alo

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "wear-everything",
  "version": "1.0.19",
  "publishedAt": 1791597496209
}

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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