Xpersona
agentCLAWHUBUnverified

穿搭种草图 Clothing Grass Planting

同款穿搭换模特换场景做种草图。穿搭图 → 社交平台风格的种草图。当用户说「种草图」「小红书风格」「换场景发帖」「达人图」时使用。 Skill: 穿搭种草图 Clothing Grass Planting Owner: dlazyai Summary: 同款穿搭换模特换场景做种草图。穿搭图 → 社交平台风格的种草图。当用户说「种草图」「小红书风格」「换场景发帖」「达人图」时使用。 Tags: latest:1.0.18 Version history: v1.0.18 | 2026-10-10T01:47:50.604Z | user 例行版本更新 2026-10-10 v1.0.17 | 2026-10-08T01:40:45.147Z | user 例行版本更新 2026-10-08 v1.0.16 | 2026-10-04T01:41:09.670Z | user 例行版本更新 2026-10-04 v1.0.15 | 2026-10-02T05:14:29.813Z | user 例行版本更新 2026-10-02 v1.0.14 | 2026-09

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.2k

Updated

Oct 10, 2026

Version

1.0.18

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
1.0.18release · observed Oct 10, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:clothing-grass-planting
  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-grass-planting/snapshot"

Documentation

CLAWHUB

146,610 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: clothing-grass-planting
version: 1.0.18
description: 同款穿搭换模特换场景做种草图。穿搭图 → 社交平台风格的种草图。当用户说「种草图」「小红书风格」「换场景发帖」「达人图」时使用。
---

# clothing-grass-planting — 相同穿搭改模特场景姿势

**穿搭不动，人 / 场景 / 姿势全换**，换成社交平台的种草风格。

和 [one-shot](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/one-shot/skill.md) 的区别：one-shot 面向电商主图（保持棚拍规范、构图不动），本技能面向**内容种草**——要的是生活感、抓拍感、氛围光，构图和景别都可以变。

---

## 生成效果示例

| 输入：原穿搭图 | 输入：场景/模特参考图 |
| --- | --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/clothing-grass-planting/source-outfit.jpg" width="240"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/clothing-grass-planting/scene-reference.jpg" width="240"> |
| `source-outfit.jpg` — 浅灰针织连衣裙 + 珍珠项链 + 托特包 + 银色高跟鞋，喷泉庭院 | `scene-reference.jpg` — 咖啡馆门口街拍，手抚头发，树影暖光 |

实际执行的命令：

```bash
dlazy gpt-image-2 \
  --prompt 'Lifestyle social-commerce photo. Image 1 shows the outfit to keep: a light-grey textured sleeveless knit mini dress with a mock neckline, a pearl choker, a cream-and-tan tote bag and silver pointed heels. Image 2 is the model, pose, scene and lighting reference: a young woman on a sunlit tree-lined street outside a coffee shop, hand in her hair, warm dappled daylight, shallow depth of field. Put the complete outfit from image 1 onto the model from image 2. Keep every garment detail faithful: same grey knit texture, same neckline and hem length, same pearl choker, same tote bag colour blocking. Reproduce image 2 for the model identity, pose, camera angle, crop, street background and colour grading. Photorealistic influencer-style photo, no text, no watermark.' \
  --images docs/clothing-grass-planting/source-outfit.jpg docs/clothing-grass-planting/scene-reference.jpg \
  --size 1024x1536 --quality medium --imageFormat jpeg \
  --save docs/clothing-grass-planting/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1536。连衣裙的浅灰针织纹理、立领、腰线与裙长，珍珠项链的珠径，托特包的米白/棕拼色全部保留；模特、抚发姿势、咖啡馆街景、树影暖光与浅景深照抄参考图。

---

## 1、能力边界

| 保持不变 | 会改变 |
| --- | --- |
| 上衣 / 下装 / 鞋 / 包 / 配饰的颜色、图案、材质、版型与搭配关系 | 模特身份、姿势与动作、场景与背景、光线与色调、机位与景别 |

| 控制方式 | 说明 |
| --- | --- |
| 参考图 | 照抄某张种草图的模特、场景、姿势与光线 |
| 自定义提示词 | 用文字描述目标场景与动作 |

**不做**：不改穿搭的任何一件单品；不生成特定真人的换脸图；不用于伪造他人肖像代言或伪造使用体验。

---

## 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 |
| --- | --- |
| ✅ 全身或大半身穿搭图 | 种草图通常要看到整套搭配 |
| ✅ 每件单品都清晰可辨 | 看不清的单品换场景后会被重绘 |
| ✅ 单人 | 多人同框模型分不清主体 |
| ❌ 商品被严重遮挡 | 挡住的部分换姿势后必然变形 |
| ❌ 已带滤镜/文字贴纸 | 会被一起带进新图，先洗干净 |

---

## 3、种草图的场景配方

种草图的说服力来自「像是随手拍的」。四个要素缺一不可：

| 要素 | 写法示例 |
| --- | --- |
| 场景 | `a sunlit tree-lined street outside a coffee shop` / `a bright bedroom with sheer curtains` / `a seaside boardwalk at golden hour` |
| 动作 | `hand in her hair` / `mid-stride walking toward the camera` / `sitting on a step holding a coffee cup

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "clothing-grass-planting",
  "version": "1.0.18",
  "publishedAt": 1791596870604
}

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 11, 2026.

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