agentmods.dev

amazon-review-analyzer

amazon-review-analyzer is a skill for Claude Code from buluslan/n8n-to-skill. It costs 136 tokens per session (1,054 once invoked), scanned A, original, MIT.

A workflow for labeling Amazon product reviews across 22 dimensions, calculating label distributions, and producing a six-part insight report. Amazon reviews are customer feedback on products sold through Amazon.

In plain words
What is it for?
It helps validate a reviews CSV, create a tagged CSV, calculate statistics, and write recommendations based on representative positive and negative reviews.
Why use it?
It turns a large review file into organized evidence about recurring problems, strengths, and possible product improvements.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 24repo 2mo ago A scan 136 tokens original MIT

Good fit It helps validate a reviews CSV, create a tagged CSV, calculate statistics, and write recommendations based on representative positive and negative reviews.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/buluslan/n8n-to-skill/amazon-review-analyzer
View source ↗ buluslan/n8n-to-skill
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 buluslan/n8n-to-skill --skill amazon-review-analyzer
Clone the repo
git clone --depth 1 https://github.com/buluslan/n8n-to-skill

Made for: Claude Code.

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 amazon-review-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/buluslan/n8n-to-skill/amazon-review-analyzer/github.svg)](https://agentmods.dev/skills/buluslan/n8n-to-skill/amazon-review-analyzer)
Your own site
<a href="https://agentmods.dev/skills/buluslan/n8n-to-skill/amazon-review-analyzer"><img src="https://agentmods.dev/badge/skills/buluslan/n8n-to-skill/amazon-review-analyzer/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 amazon-review-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/buluslan/n8n-to-skill/amazon-review-analyzer"><img src="https://agentmods.dev/badge/skills/buluslan/n8n-to-skill/amazon-review-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,054 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 $0.00136 $0.01054
Opus 5.5 $0.00054 $0.00422
Sonnet 5.5 $0.00027 $0.00211
Haiku 4.5 $0.00014 $0.00105

Measured 2mo ago against content hash a9b2edf6a0bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

amazon-review-analyzer 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/io.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

examples/example-output/amazon-review-analyzer/SKILL.md · 49 lines

What it actually says

amazon-review-analyzer

定位

给跨境卖家：把一批亚马逊商品评论变成「每条评论的 22 维度标签 + 分布统计 + 6 章深度洞察报告」，支撑产品迭代决策（改款方向、差评应对、卖点提炼）。

触发

用户给一个评论 CSV 路径，并说「分析评论 / 打标签 / 出洞察 / 挖卖点 / 评论分析」任一关键词时启动。

输入

  • 评论 CSV（utf-8）。期望列：标题/标题(翻译)、内容/内容(翻译)（核心文本）、星级、VP评论；英文列名 title/text/rating/vp 同样识别。
  • 必须有评论文本列（内容 / 内容(翻译) / text 任一），否则报错终止。

输出（3 个文件，落执行目录的 output/）

  • tagged.csv：原评论列 + 22 个标签列
  • stats.csv：每个标签列的值分布（计数 + 占比）
  • insight.md：6 章深度洞察报告

核心能力

  1. 读 CSV 校验（脚本 scripts/io.py count）
  2. 22 维度打标（Agent 逐条评论，维度体系见 references/tagging.md）
  3. 标签统计（脚本 scripts/io.py stats，对 22 个标签列算分布）
  4. 6 章洞察生成（Agent，框架见 references/insight.md）

流程

  1. 校验：python3 scripts/io.py count <reviews.csv> → 打印行数 + 列名 → 确认有评论文本列。
  2. 打标：逐条评论按 references/tagging.md 输出 22 维 JSON 标签 → 汇总成 output/tagged.csv（原列 + 22 标签列）→ 打印进度（每 10 条一次）。
  3. 统计：python3 scripts/io.py stats output/tagged.csv output/stats.csv → 对 22 个标签列算值分布 → 写 stats.csv。
  4. 洞察：读 stats.csv + 精选正负各 Top3 评论，按 references/insight.md 的 6 章框架生成报告 → 写 output/insight.md。

凭证边界

无。目标对等重写后，原 workflow 的 Gemini API key 与 googleSheets OAuth 均已消化——LLM 能力由执行环境（Claude）直接提供，存储改为本地 CSV，无需任何外部凭证。

失败降级

  • CSV 缺评论文本列（内容 / 内容(翻译) / text）→ 报错并指明缺哪列，终止。
  • 单条评论 AI 打标失败 → 重试 3 次后该条标签全填 未提及 / 不明，标 [TAG_FAILED] 后继续，不阻塞整体。
  • 评论数 < 5 → 只出 tagged.csv + stats.csv，跳过洞察（样本不足），在 insight.md 注明"样本不足，未生成洞察"。
  • 单条评论 > 2000 字 → 截断到 2000 字打标，tagged.csv 标 [truncated]。
Files

What ships with it

4 files 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.

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. 2mo ago First seen · 49 lines · 136 tokens per session scan A a9b2edf6a0bd

Subscribe to this mod's changes

amazon-review-analyzer is a skill published in the GitHub repository buluslan/n8n-to-skill (24 stars, last pushed 2mo ago), licensed MIT. It adds 136 tokens to every session and 1,054 once invoked, about $0.0005 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.

Analytics and metricsE-commerce
Related

amazon-review-analyzer compared with skills from other repositories

amazon-review-analyzer next to 6 skills from other repositories
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