ecom-amazon-a-plus

ecom-amazon-a-plus is a skill for Claude Code, Codex from ddcat-ai/open-ai-canvas. It costs 34 tokens per session (328 once invoked), scanned A, original, MIT.

A guide for planning Amazon A+ content, the extra product-detail modules shown on an Amazon listing. It arranges brand introduction, feature explanations, lifestyle scenes, details, and known specifications while keeping the product and visual style consistent.

In plain words
What is it for?
It is for deciding each module’s main image, text placement, spacing, typography, colours, proportions, and mobile readability.
Why use it?
It gives a product page a clear structure without inventing certifications, comparisons, after-sales promises, or specifications.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for deciding each module’s main image, text placement, spacing, typography, colours, proportions, and mobile readability.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus
About the project

影策 is an open-source workspace for AI film and short-video production, using a freeform canvas and structured workflows to organize scripts, characters, scenes, storyboards, media assets, and generated text, images, video, and audio. It is for creators who manage audiovisual projects through local or trusted deployments, with features for task tracking, asset management, timelines, and AI-agent workflows.

ddcat-ai/open-ai-canvas · 1,703 stars · on GitHub · yingce.tv

Install

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.

Any agent
npx skills add ddcat-ai/open-ai-canvas --skill ecom-amazon-a-plus
Clone the repo
git clone --depth 1 https://github.com/ddcat-ai/open-ai-canvas

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ecom-amazon-a-plus

README.md
[![agentmods](https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus/github.svg)](https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus)
Your own site
<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus/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.

agentmods 80×15 button for ecom-amazon-a-plus

Your own site · 80×15
<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ecom-amazon-a-plus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 328 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00328
Opus 5.5 $0.00014 $0.00131
Sonnet 5.5 $0.00007 $0.00066
Haiku 4.5 $0.00003 $0.00033

Measured 2d ago against content hash 25bd391ccc2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ecom-amazon-a-plus 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 2d 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.

backend/internal/ecommerceskills/packages/ecom-amazon-a-plus/SKILL.md · 23 lines

What it actually says

A+模块叙事

输入与适用范围

使用本轮商品事实、参考素材职责、用户提示词、目标平台及已确认风格。

执行方法

按品牌开场、功能解释、生活场景、细节和已知规格安排模块;继承同一商品锚点与视觉系统,按用户实际张数取舍,不套固定模块数量。

图片执行要求

每模块明确主视觉、留白、标题位置、边距与已确认文案;采用用户选定比例和尺寸,不把来源示例尺寸当作当前平台强制要求。

交付核对

检查各模块商品身份、字体与配色一致,移动端文字清晰,未凭空增加比较结果、认证或售后。

来源与适配

https://github.com/xianyu110/ecommerce-image-skills/blob/f4566fdc812584425728862ad8e08c90883d81ee/skills/amazon-a-plus/SKILL.md 版本:f4566fdc812584425728862ad8e08c90883d81ee。按 MIT 许可改编;仅保留适合本工作台的创意方法,不导入来源脚本、指定供应商、固定页数或平台示例阈值。

Changes

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.

  1. 2d ago First seen · 23 lines · 34 tokens per session scan A 25bd391ccc2e

Subscribe to this mod's changes

ecom-amazon-a-plus is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,703 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 328 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 2026-11-05.

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