Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aliang2052/aliang-skills --skill aliang-product-detail-photosgit clone --depth 1 https://github.com/aliang2052/aliang-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aliang2052/aliang-skills/aliang-product-detail-photos)<a href="https://agentmods.dev/skills/aliang2052/aliang-skills/aliang-product-detail-photos"><img src="https://agentmods.dev/badge/skills/aliang2052/aliang-skills/aliang-product-detail-photos/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aliang2052/aliang-skills/aliang-product-detail-photos"><img src="https://agentmods.dev/badge/skills/aliang2052/aliang-skills/aliang-product-detail-photos.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00884 |
| Opus 5.5 | $0.00013 | $0.00354 |
| Sonnet 5.5 | $0.00007 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
aliang-product-detail-photos scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
商品详情图生成器
依赖: 阿里云百炼 CLI (
bl),需配置DASHSCOPE_API_KEY。
触发词
当用户提到"商品详情图"、"电商主图"、"产品图"、"去掉背景"、"做产品宣传图"时使用。
工作流
第一步:确认需求
向用户确认:
- 图片数量 — 需要几张(用户必须指定)
- 产品图片 — 原图路径
- 图片比例 — 1:1 方图 / 3:4 竖图 / 16:9 横图(默认 3:4)
- 风格偏好 — 简约 / 温馨 / 自然 / 科技感 等(默认简约)
- 输出目录 — 默认
输出/
第二步:产品理解与场景规划
用 bl text chat 分析产品,规划拍摄方案:
bl text chat --message "你是一位专业的电商摄影师。请分析产品图片,规划商品详情图拍摄方案。
产品图片:{product_image_path}
需要生成:{n}张,风格:{style}
请输出规划表,将{n}张图片拆分为「角度」+「场景」的组合,每张说明展示角度、场景背景、构图要点、光线氛围。"
向用户展示规划表,等待确认。
第三步:去除产品背景
bl image edit \
--image {product_image_path} \
--prompt "Remove the background completely, keep only the product itself with clean edges, output on solid white background" \
--model qwen-image-2.0-pro \
--out-dir {output_dir} \
--out-prefix "product_no_bg"
第四步:逐张生成详情图
对每张图,用去背景后的产品图放入场景(效果比纯文本生成更准确):
bl image edit \
--image {product_no_bg_path} \
--prompt "Place this product naturally in {scene_description}, {composition_details}, {lighting_details}, professional product photography, sharp focus, no text, no watermark, no logo" \
--model qwen-image-2.0-pro \
--size "{ratio}" \
--out-dir {output_dir} \
--out-prefix "product_{index}"
场景建议:
- 食品/饮料 → 餐桌、厨房、自然光线
- 服装/鞋帽 → 模特穿搭、纯色背景、生活场景
- 数码产品 → 简约科技风、办公桌面
- 儿童用品 → 温馨家居、明亮色彩
- 户外用品 → 自然风光、使用场景
第五步:整理输出
输出报告:
✅ 商品详情图生成完成
📁 输出目录:{output_dir}/{product_name}/
📊 共生成 {n} 张图片:
- product_no_bg.png — 去背景产品图
- product_01.png — [角度] + [场景]
...
关键命令
| 操作 | 命令 |
|---|---|
| 去背景 | bl image edit --image <path> --prompt "Remove background..." --model qwen-image-2.0-pro |
| 产品入场景 | bl image edit --image <no_bg_path> --prompt "Place in {scene}..." --model qwen-image-2.0-pro --size "1:1" |
| 纯文本生成 | bl image generate --prompt "<description>" --model qwen-image-2.0-pro --size "3:4" |
| 产品分析 | bl text chat --message "<analysis prompt>" |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 94 lines · 33 tokens per session scan A d735fc1662aa
aliang-product-detail-photos is a skill published in the GitHub repository aliang2052/aliang-skills (30 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 884 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
aliang-product-detail-photos compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| aliang-product-detail-photosaliang2052/aliang-skills | Skill | 30 | 4mo ago | MIT | A | 33 | Claude Code, Codex |
| workflowsdramaclaw/dramaclaw | Skill | 7.3k | 2d ago | none | A | 56 | Claude Code, Codex |
| ecom-details-imageanymouschina/TapCanvas | Skill | 709 | 4d ago | MIT | A | 200 | Claude Code |
| freezonedramaclaw/dramaclaw | Skill | 7.3k | 2d ago | none | A | 49 | Claude Code, Codex |
| tiktok-shop-videovargHQ/skills | Skill | 21 | yesterday | MIT | A | 233 | Claude Code |
| ai-model-expert-1688-ecommerce-video-generation-editingwubin1836/ai-hive-agent-skills | Skill | 23 | 27d ago | MIT | A | 330 | Codex |
| ai-model-expert-1688-ecommerce-image-generation-editingwubin1836/ai-hive-agent-skills | Skill | 23 | 27d ago | MIT | A | 326 | Codex |