agentCLAWHUBUnverified
跨境本地化 Cross-border Localize
跨境一套素材多区域本地化。一套素材 → 多语言文案、尺码换算表、区域合规标识。当用户说「跨境本地化」「多语言」「翻译上架」「海外版本」「英文版主图」时使用。 Skill: 跨境本地化 Cross-border Localize Owner: dlazyai Summary: 跨境一套素材多区域本地化。一套素材 → 多语言文案、尺码换算表、区域合规标识。当用户说「跨境本地化」「多语言」「翻译上架」「海外版本」「英文版主图」时使用。 Tags: latest:1.0.16 Version history: v1.0.16 | 2026-10-10T01:48:54.588Z | user 例行版本更新 2026-10-10 v1.0.15 | 2026-10-08T01:41:35.113Z | user 例行版本更新 2026-10-08 v1.0.14 | 2026-10-04T01:41:50.339Z | user 例行版本更新 2026-10-04 v1.0.13 | 2026-10-02T05:15:00.190Z | user 例行版本更新 2026-10-02 v1.0.
OpenClaw
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:cross-border-localize- 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.
- 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-cross-border-localize/snapshot"
Documentation
CLAWHUB
147,490 characters of source documentation, loaded on request.
Extracted files
5 files captured from the source.
SKILL.md
--- name: cross-border-localize version: 1.0.16 description: 跨境一套素材多区域本地化。一套素材 → 多语言文案、尺码换算表、区域合规标识。当用户说「跨境本地化」「多语言」「翻译上架」「海外版本」「英文版主图」时使用。 --- # cross-border-localize — 跨境本地化 同一件商品卖去不同国家，**不是把中文文案翻译一遍就完事**。 尺码体系不同、合规标识不同、审美偏好不同、平台规格也不同。 这个技能把一套素材铺成多个区域可直接上架的版本。 --- ## 一、能力边界 | 能做 | 说明 | | --- | --- | | 文案本地化 | 不是直译，是按当地表达习惯重写 | | 尺码换算 | CN / US / UK / EU / JP 对照表 | | 区域版主图 | 带当地语言文案排版的主图 | | 平台规格适配 | 各区域主流平台的图片规格 | | 合规标识提示 | 提示该区域常见的必需标识 | | 不能做 | 说明 | | --- | --- | | 法律意见 | 合规标识只做提示，正式上架前请咨询专业渠道 | | 保证翻译无误 | 重要文案请母语者过一遍 | --- ## 二、本地化不等于翻译 | 维度 | 中国站 | 北美站 | 日本站 | | --- | --- | --- | --- | | 文案风格 | 卖点密集、数字多 | 简洁、强调场景与个性 | 礼貌、克制、细节详尽 | | 主图信息量 | 可带较多文字 | 主图通常禁文字 | 信息密度中等 | | 尺码 | S/M/L + 具体厘米 | XS–XXL，需换算 | 号数体系不同，偏小 | | 模特 | 亚洲面孔 | 需要多元面孔 | 亚洲面孔 | | 促销表达 | 直给折扣 | 强调价值与保障 | 强调品质与售后 | **最常见的错误**：把中文主图的密集文案直译贴到 Amazon 主图上—— 主图禁文字，直接驳回。 --- ## 三、尺码换算 上装（女装，仅供参考，具体以品牌尺码表为准）： | CN | US | UK | EU | JP | | --- | --- | --- | --- | --- | | S / 155-160 | 2-4 | 6-8 | 34-36 | 7-9 | | M / 160-165 | 6-8 | 10-12 | 38-40 | 11-13 | | L / 165-170 | 10-12 | 14-16 | 42-44 | 15-17 | | XL / 170-175 | 14-16 | 18-20 | 46-48 | 19-21 | **永远同时给具体厘米数。** 各品牌版型差异大，字母码不可靠， 胸围/肩宽/衣长的厘米数才是买家真正要的，也是降低退货率最有效的一项。 --- ## 四、区域平台规格 | 区域 | 主流平台 | 图片规则要点 | | --- | --- | --- | | 北美 | Amazon | 主图纯白 RGB(255,255,255)、商品占 ≥85%、禁文字水印 | | 东南亚 | Shopee / Lazada | 方图，体积限制紧（≤2MB） | | 全球 | TikTok Shop | 1:1 或 3:4，禁边框水印 | | 全球 | Temu | 方图、干净背景、主图禁促销文字 | | 独立站 | Shopify | 无硬性限制，2048 方图便于放大镜 | 各区域版本出完，逐个过校验： ```bash python3 scripts/check_listing.py out/us/*.jpg --platform amazon python3 scripts/check_listing.py out/sea/*.jpg --platform shopee python3 scripts/check_listing.py out/global/*.jpg --platform tiktok-shop ``` --- ## 五、工具调用 **北美版主图（无文字，走合规路线）** ```bash node scripts/gen.mjs --task cross-border-localize --brand examples/brand.yaml \ --prompt 'E-commerce main image of an olive cable-knit crewneck sweater on a model. Diverse casting suitable for the North American market. Pure seamless white background, RGB 255,255,255. Product fills 88% of the frame. No text, no logo, no watermark.' \ --images docs/flat-lay/garment-flatlay.jpg docs/flat-lay/pose-reference.jpg \ --save out/us/A001-main.jpg python3 scripts/check_listing.py out/us/A001-main.jpg --platform amazon ``` **日本站详情图（带日文排版）** ```bash node scripts/gen.mjs --task cross-border-localize --brand examples/brand.yaml \ --prompt 'E-commerce detail module for a Japanese marketplace listing. Olive cable-knit sweater. Clean vertical layout with generous margins, restrained typography. Japanese text overlay reading: 「厚手ケーブルニット」「三層構造で暖かい」. Soft neutral background. No watermark.' \ --images docs/flat-lay/garment-flatlay.jpg \ --save out/jp/A001-detail.jpg ``` ⚠️ **模型生成的非英文文字经常出错**（尤其日文汉字与假名混排）。 带文字的版本一律要人工核对，或改用「生成无文字底图 + 后期排版」的路子。 --- ## 六、执行流程 1. **问清楚卖去哪些区域**，别默认「全球」。 2. **先做无文字版本**。无文字主图能通行大多数平台，是最省事的公共底座。 3. **按区域派生**：需要文字的单独出，出完人工核对文字。 4. **逐区域过合规校验**，规格不同不能混用。 5. **尺码表单独出一张**，同时给字母码和厘米数。 6. **合规标识**：提示
_meta.json
{
"ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
"slug": "cross-border-localize",
"version": "1.0.16",
"publishedAt": 1791596934588
}references/platform-specs.md
<!-- 由 scripts/build-skills.mjs 从 shared/references/platform-specs.md 同步生成，不要直接改这里。 --> # 平台图片规格（可机检子集） `scripts/check_listing.py` 内置的规则来源与口径。**平台规则会变，以各平台最新官方文档为准**； 需要覆盖时写一份 JSON 用 `--rules` 传入，结构与下表字段一一对应。 --- ## 字段含义 | 字段 | 含义 | | --- | --- | | `pure_white_bg` | 是否要求纯白 RGB(255,255,255) 背景 | | `bg_tolerance` | 判定「纯白」允许的单通道偏差 | | `bg_coverage` | 边缘一圈需要有多大比例落在容差内 | | `min_long_side` / `recommend_long_side` / `max_long_side` | 最长边像素 | | `min_occupancy` | 商品包围盒面积 ÷ 画面面积 的下限 | | `allow_alpha` | 是否允许透明通道 | | `allow_border` | 是否允许描边 / 外框 | | `formats` / `max_bytes` / `aspect` | 允许格式、体积上限、允许比例 | --- ## 内置规则 | 平台 | 纯白底 | 最长边（下限 / 建议） | 主体占比 | 比例 | 体积上限 | | --- | --- | --- | --- | --- | --- | | `amazon` | 是 | 1000 / 1600 | ≥ 85% | 不限 | 10 MB | | `tiktok-shop` | 否 | 800 / 1600 | ≥ 60% | 1:1 或 3:4 | 5 MB | | `temu` | 是 | 800 / 1350 | ≥ 70% | 1:1 | 3 MB | | `shopee` | 否 | 500 / 1024 | ≥ 55% | 1:1 | 2 MB | | `shopify` | 否 | 1024 / 2048 | 不限 | 不限 | 20 MB | | `taobao` | 是 | 800 / 1200 | ≥ 70% | 1:1 | 3 MB | --- ## 几个容易踩的点 - **透明 PNG**：Amazon 会把透明像素转成黑色。永远压平成白底 JPEG 再传。 - **「白底」不等于「看起来是白的」**：棚拍的浅灰墙（约 RGB 208）肉眼像白，机检直接判不合格。 - **主体占比**：留白过多是最常见的驳回原因之一，比分辨率不够更常见。 - **文字 / 水印 / 拼图**：像素层测不了，交给 `detect-task` 的视觉模型或人工。 - **自动修复的边界**：`--fix` 能压白底、按占比重构画布、补分辨率、压体积； 它**不会**修图，也不会去水印——那是 `item-repair` 和 `remove-watermark` 的活。
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 },
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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.