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 buluslan/n8n-to-skill --skill amazon-review-analyzergit clone --depth 1 https://github.com/buluslan/n8n-to-skillWrote 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/buluslan/n8n-to-skill/amazon-review-analyzer)<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.
<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>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 | $0.00136 | $0.01054 |
| Opus 5 | $0.00068 | $0.00527 |
| Sonnet 5 | $0.00027 | $0.00211 |
| Haiku 4.5 | $0.00014 | $0.00105 |
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 12d 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
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
- 读 CSV 校验（脚本
scripts/io.py count） - 22 维度打标（Agent 逐条评论，维度体系见
references/tagging.md） - 标签统计（脚本
scripts/io.py stats，对 22 个标签列算分布） - 6 章洞察生成（Agent，框架见
references/insight.md）
流程
- 校验：
python3 scripts/io.py count <reviews.csv>→ 打印行数 + 列名 → 确认有评论文本列。 - 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）。 - 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。 - 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]。
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.
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.
- 12d ago First seen · 49 lines · 136 tokens per session scan A a9b2edf6a0bd
amazon-review-analyzer is a skill published in the GitHub repository buluslan/n8n-to-skill (22 stars, last pushed 24d ago), licensed MIT. It adds 136 tokens to every session and 1,054 once invoked, about $0.0007 per session on Opus 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.
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