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UGC 口播种草视频 UGC Testimonial

UGC 口播种草视频。商品 + 人设 → 口播脚本与成片，达人自拍质感。当用户说「口播视频」「种草视频」「达人风格」「UGC」「真人推荐」时使用。 Skill: UGC 口播种草视频 UGC Testimonial Owner: dlazyai Summary: UGC 口播种草视频。商品 + 人设 → 口播脚本与成片，达人自拍质感。当用户说「口播视频」「种草视频」「达人风格」「UGC」「真人推荐」时使用。 Tags: latest:1.0.16 Version history: v1.0.16 | 2026-10-10T01:57:48.468Z | user 例行版本更新 2026-10-10 v1.0.15 | 2026-10-08T01:48:42.323Z | user 例行版本更新 2026-10-08 v1.0.14 | 2026-10-04T01:48:05.559Z | user 例行版本更新 2026-10-04 v1.0.13 | 2026-10-02T05:19:34.924Z | user 例行版本更新 2026-10-02 v1.0.12 | 2

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.16

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

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 11, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 11, 2026
Adoption signal
1.1K downloadsadoption · observed Oct 11, 2026
Latest release
1.0.16release · observed Oct 10, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:ugc-testimonial
  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-ugc-testimonial/snapshot"

Documentation

CLAWHUB

148,044 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: ugc-testimonial
version: 1.0.16
description: UGC 口播种草视频。商品 + 人设 → 口播脚本与成片，达人自拍质感。当用户说「口播视频」「种草视频」「达人风格」「UGC」「真人推荐」时使用。
---

# ugc-testimonial — UGC 口播种草

投流素材里转化最好的一类往往不是精致广告，而是**看起来像真人随手拍的推荐**。

这个技能生成那种质感：手持、家里的光、说人话。

---

## 一、能力边界

| 能做 | 说明 |
| --- | --- |
| 口播脚本 | 商品 + 人设 → 一段能照着念的话 |
| 分镜 | 口播镜头 + 商品特写的交替结构 |
| UGC 质感 | 手持轻晃、室内自然光、非专业构图 |
| 多人设 | 同一商品换人设出多版做 A/B |

| 不能做 | 说明 |
| --- | --- |
| 对口型 | 生成模型做不好唇形同步，成片建议配旁白而不是同期声 |
| 冒充真人 | 不要声称是真实买家评价，见下方「合规」 |

---

## 二、合规先说

UGC 风格 ≠ 伪造评价。生成的内容**不能声称是真实买家的真实使用体验**。

| 可以 | 不可以 |
| --- | --- |
| AI 生成的演示型口播 | 冒充具名真实买家的评价 |
| 「这件我穿了一周」的场景演绎 | 伪造好评截图、伪造买家秀 |
| 按平台要求标注 AI 生成 | 隐瞒 AI 生成身份（部分平台强制标注） |

各平台对 AI 生成内容的标注要求不同，投放前确认目标平台的规则。

---

## 三、人设与脚本

**人设三要素**：谁、什么场景、为什么可信。

| 人设 | 开场白范式 |
| --- | --- |
| 通勤白领 | 「每天挤地铁，我需要一件不用打理的……」 |
| 学生党 | 「预算两百，我对比了五家……」 |
| 宝妈 | 「带娃根本没时间收拾自己，所以……」 |
| 健身人群 | 「练完一身汗，这件……」 |

**脚本骨架（15 秒）**

```
0-3s   钩子：说一个具体的痛点，不说商品
3-8s   转折：为什么这个能解决 —— 给一个可验证的细节
8-12s  展示：镜头切到商品特写
12-15s 落点：给一个行动理由，语气随意
```

**写口播的三条铁律**

1. **说具体的**。「特别暖和」无效，「零下五度我只穿了这一件」有效。
2. **留犹豫**。真人说话有停顿和自我修正，全是完美长句就假了。
3. **不念参数**。参数放字幕，嘴里说感受。

---

## 四、工具调用

```bash
export DLAZY_VIDEO_MODEL=<你账号里可用的视频模型 ID>

node scripts/video.mjs --mode storyboard --task ugc-testimonial \
  --board examples/ugc-board.json --outdir out/ugc --subtitles
```

分镜里交替口播镜头与商品镜头：

```json
{
  "shots": [
    { "id": "talk1", "seconds": 4, "image": "assets/model/face-a.jpg",
      "prompt": "Handheld selfie-style shot, young woman talking to camera in a bright apartment, slight natural camera shake, warm window light, casual home background. She is wearing the olive cable-knit sweater.",
      "caption": "零下五度，我就穿了这一件" },
    { "id": "detail1", "seconds": 3, "image": "docs/flat-lay/garment-flatlay.jpg",
      "prompt": "Handheld close-up of the sweater texture, phone-camera look, natural indoor light.",
      "caption": "粗棒针织，比看起来厚" },
    { "id": "talk2", "seconds": 4, "image": "assets/model/face-a.jpg",
      "prompt": "Handheld selfie-style shot, same woman, she pulls the collar up and smiles, same apartment.",
      "caption": "洗了三次也没变形" }
  ]
}
```

**关键 prompt 词**：`handheld`、`selfie-style`、`slight natural camera shake`、
`phone-camera look`、`natural indoor light`、`casual` —— 这些制造「不专业」的质感。
不要写 `cinematic`、`professional lighting`、`studio`，那会把 UGC 感抹掉。

---

## 五、执行流程

1. **先定人设**。问用户卖给谁，人设跟着买家画像走。
2. **写脚本给用户确认**。口播文案比画面更决定转化。
3. **锁脸**。多个口播镜头必须是同一个人，用 [brand-kit](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/brand-kit/skill.md) 的 `model.reference`。
4. **`--dry-run` 过一遍**，再真跑。
5. **配旁白**。生成的视频不做唇形同步，成片后用剪辑软件配一条旁白，比同期声自然。

---

## 六、常见问题

| 现象 | 原因 | 怎么办 |
| --- | --- | --- |
| 看起来还是像广告 | prompt 里有 `cinematic` / `professional` | 换成 handheld / phone-camera look |
| 几个镜头不是同一个人 | 没锁脸 | 用 `--brand` 带 `model.reference` |
| 嘴型对不上 | 模型不做唇形同步 | 别用同期声，配旁白；或改用不露脸的手持镜头 |
| 表情僵硬 | 人脸是视频模型弱项 | 缩短口播镜头，多用商品特写切换 |

---

## Tips

- **口播镜头短、商品镜头长**。人脸生成越久越容易崩，3–4 秒一切。
- **同一脚本换人设出三版**做 A/B，投流看数据说话。
- **字幕必须有

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "ugc-testimonial",
  "version": "1.0.16",
  "publishedAt": 1791597468468
}

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

references/video-backends.md

<!-- 由 scripts/build-skills.mjs 从 shared/references/video-backends.md 同步生成，不要直接改这里。 -->
# 视频后端配置

图像模型的默认值写死在 `lib/tasks.json` 里，视频模型没有——**因为各家的视频模型
ID 差异大、更新快，写死只会误导**。所以视频技能要求你显式指定。

## 指定模型

```bash
export DLAZY_VIDEO_MODEL=<你账号里可用的视频模型 ID>
# 或每次调用时
node scripts/video.mjs --mode clip --task main-image-video --model <id> ...
```

没设会直接报错，不会拿一个猜的模型名去跑。

## 各后端

| 后端 | 怎么配 | 说明 |
| --- | --- | --- |
| `dlazy` | `DLAZY_VIDEO_MODEL` | 用 `dlazy --help` 看当前账号可用的视频工具 |
| `fal` | `FAL_KEY` + `GEN_MODEL_FAL=<视频模型路径>` | 产出在 `videos[]` 或 `video.url` |
| `replicate` | `REPLICATE_API_TOKEN` + `GEN_MODEL_REPLICATE=<owner/model>` | 产出为 URL |

图生视频时参考图走 `--images`，与图像技能一致。

## 合成依赖

拼接与字幕需要 ffmpeg：

```bash
brew install ffmpeg        # macOS
apt install ffmpeg         # Debian/Ubuntu
```

没装也能跑——片段照常生成，脚本会输出 `concat.txt`，装好后一条命令补拼。

## 字幕的三级降级

1. **烧录进画面**：需要 ffmpeg 带 libass。很多发行版的预编译包没有。
2. **软字幕轨**：`-c:s mov_text` 封进 MP4，播放器可开关。几乎总能成。
3. **都不行**：`.srt` 留在产物目录旁边，可导入剪辑软件。

脚本自动逐级尝试，不用你判断。查本机是否支持烧录：

```bash
ffmpeg -hide_banner -filters | grep ' subtitles '
```

## 分镜文件

```json
{
  "shots": [
    { "id": "s1", "seconds": 3, "image": "main.jpg",
      "prompt": "Slow push-in on the product, soft light sweeps across the surface.",
      "caption": "三层加厚，零下也不怕" }
  ]
}
```

`caption` 会按 `seconds` 累加时间轴自动生成 SRT，不用手对时间码。

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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      "label": "Vendor",
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    },
    {
      "factKey": "handshake_status",
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      "label": "Handshake status",
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      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-ugc-testimonial/trust",
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      "sourceType": "trust",
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  ],
  "events": [
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      "title": "Release 1.0.16",
      "description": "例行版本更新 2026-10-10",
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      "sourceType": "release",
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    }
  ]
}

Record generated Oct 11, 2026.

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