agentCLAWHUBUnverified
上架前合规校验 Platform Compliance
上架前平台合规校验与自动修复。待上架图 → Amazon / TikTok Shop / Temu / Shopee / 淘宝 各平台的通过或驳回风险报告，可一键修成合规图。当用户说「会不会被驳回」「合规检查」「白底不达标」「主图规格」「传上去被拒」时使用。 Skill: 上架前合规校验 Platform Compliance Owner: dlazyai Summary: 上架前平台合规校验与自动修复。待上架图 → Amazon / TikTok Shop / Temu / Shopee / 淘宝 各平台的通过或驳回风险报告，可一键修成合规图。当用户说「会不会被驳回」「合规检查」「白底不达标」「主图规格」「传上去被拒」时使用。 Tags: latest:1.0.18 Version history: v1.0.18 | 2026-10-10T01:55:09.049Z | user 例行版本更新 2026-10-10 v1.0.17 | 2026-10-08T01:46:53.072Z | user 例行版本更新 2026-10-08 v1.0.16 | 2026-10-04T01:46:31.928Z | user 例行版本更新 2026-10-04 v1.0.15 | 2026
OpenClaw
Rank
62
Safety
84
Downloads
1.2k
Updated
Oct 11, 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 10/11/2026.
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
- 1.2K downloadsadoption · observed Oct 11, 2026
- Latest release
- 1.0.18release · observed Oct 10, 2026
- Handshake status
- UNKNOWNsecurity
Install and run
Setup complexity: low.
clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:platform-compliance- 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-platform-compliance/snapshot"
Documentation
CLAWHUB
147,413 characters of source documentation, loaded on request.
Extracted files
5 files captured from the source.
SKILL.md
--- name: platform-compliance version: 1.0.18 description: 上架前平台合规校验与自动修复。待上架图 → Amazon / TikTok Shop / Temu / Shopee / 淘宝 各平台的通过或驳回风险报告，可一键修成合规图。当用户说「会不会被驳回」「合规检查」「白底不达标」「主图规格」「传上去被拒」时使用。 --- # platform-compliance — 上架前合规校验 图片好不好看是一回事，**平台收不收**是另一回事。 这个技能只回答后者：这张图传上去会不会被驳回，卡在哪一条，怎么改。 和 [detect-task](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/detect-task/skill.md) 的分工： detect-task 用视觉模型判断「像不像真的」，主观、要花算力； 本技能读像素做客观判定，不花算力、毫秒级、结论可复现。**两个都过才叫能投。** --- ## 生成效果示例 拿本仓库 [flat-lay](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/flat-lay/skill.md) 的实际产出图，按 Amazon 主图规则检： ```bash python3 scripts/check_listing.py docs/flat-lay/example-output.jpg --platform amazon ``` **真实输出** ``` docs/flat-lay/example-output.jpg · Amazon 主图 · 有驳回风险 1024×1536 JPEG 0.25MB · 背景 RGB(208, 208, 208) · 主体占比 100.0% ! 分辨率 最长边 1536px，达标但不足以触发放大镜 ✓ 画面比例 1024:1536 ✗ 纯白背景 边缘仅 0.0% 为纯白，背景基色约 RGB(208, 208, 208) ✓ 主体占比 包围盒占画面 100.0% ✓ 透明通道 无 alpha ✓ 边框 无描边 ✓ 文件格式 JPEG ✓ 文件体积 0.25 MB ✓ 色彩模式 RGB ? 文字 / 水印 / 拼图 像素层判不了，交给 detect-task 或人工过一眼 ``` 这张图肉眼看是「浅灰棚拍背景」，很干净，但 **Amazon 主图要求精确 RGB(255,255,255)**， 208 的灰会被判不合格。这类问题人眼几乎发现不了，机检一秒钟出结论。 一键修： ```bash python3 scripts/check_listing.py docs/to-3d/example-output.jpg --platform amazon --fix out/ ``` ``` out/example-output-fixed.jpg · Amazon 主图 · 通过 1600×1402 JPEG 0.50MB · 背景 RGB(255, 255, 255) · 主体占比 85.1% ``` 压白底 → 按 85% 目标重构画布 → 补到 1600px → 存合规 JPEG，一步到位。 --- ## 一、能力边界 | 能做 | 说明 | | --- | --- | | 客观规格判定 | 背景纯度、主体占比、分辨率、比例、透明通道、描边、格式、体积、色彩模式 | | 多平台规则 | Amazon / TikTok Shop / Temu / Shopee / Shopify / 淘宝，可自定义 | | 自动修复 | 压白底、按目标占比重构画布、补分辨率、压到体积上限 | | 批量 | 一次传多张，或配合 [batch-image](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/batch-image/skill.md) 整批过 | | 退出码 | 0 = 可投；1 = 有驳回风险，便于接进 CI 或流水线 | | 不能做 | 去哪 | | --- | --- | | 判断有没有文字 / 水印 / 拼图 | [detect-task](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/detect-task/skill.md) 的视觉模型 | | 判断商品有没有崩 | [detect-task](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/detect-task/skill.md) | | 去水印 | [remove-watermark](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/remove-watermark/skill.md) | | 修图、去褶皱 | [item-repair](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/item-repair/skill.md) | **自动修复的边界**：`--fix` 只做几何与色彩层面的合规化，它不会替你修图。 如果原图本身是糊的、崩的、带水印的，修完还是糊的崩的带水印的。 --- ## 二、平台规则 | 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 | **平台规则会变。** 上表是可机检的子集，以各平台最新官方文档为准。 需要改用自己的口径，写一份 JSON 用 `--rules` 传入，字段说明见
_meta.json
{
"ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
"slug": "platform-compliance",
"version": "1.0.18",
"publishedAt": 1791597309049
}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.