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 Orkas-AI/Orkas-Awesome-AgentSkills --skill merch-reviewgit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/merch-review)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/merch-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-review/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/orkas-ai/orkas-awesome-agentskills/merch-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-review.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 | $0.00035 | $0.00987 |
| Opus 5 | $0.00017 | $0.00494 |
| Sonnet 5 | $0.00007 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
merch-review 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
商品评论分析
用于电商评论和用户反馈数据分析。核心任务是从用户提供或合法导出的评论中识别口碑结构、差评原因、痛点主题和可执行改进方向。
何时使用
- 用户上传淘宝、京东、拼多多、Amazon、Shopee、Shopify 等平台的评论表格、CSV、订单评价导出或授权 API 返回数据。
- 用户希望分析好评/中评/差评分布、情感、差评归因、产品痛点、物流客服问题、竞品评论差异。
- 用户要把评论分析结果转成商品优化、客服 FAQ、主图/详情页优化、listing 改写或新品迭代建议。
不用于默认抓取受限平台评论、不绕过登录/反爬、不输出未脱敏个人信息、不保证建议一定提升评分或销量。
如何调用
- 先确认数据来源是否合法：用户上传、平台后台导出、授权 API 或公开可访问数据。来源不清时先要求说明。
- 读取字段并识别商品、店铺、平台、评分、评论正文、时间、规格、用户标识、订单号、追评、图片/视频标记。
- 对敏感字段脱敏：用户名、手机号、订单号、地址、昵称 ID 等只保留必要聚合信息。使用
references/review-data-privacy.md。 - 统计评论量、平均评分、好评/中评/差评分布、时间趋势和平台/商品/店铺分组。
- 按
references/voc-taxonomy.md聚类主题，并识别样本偏差。 - 按
references/root-cause-and-actions.md做差评归因和改进行动映射。 - 没有评分字段时，情感分类标为推断；样本过少或只包含差评时，不代表整体口碑。
- 返回结构使用
references/voc-output-templates.md。
返回格式
- 数据来源和字段识别说明。
- 样本概览：评论量、时间范围、平台/商品/店铺分布。
- 评分和情感分布。
- 高频好评驱动和高频差评主题。
- 差评归因表：主题、证据片段、占比、影响、建议动作。
- 多商品/多店铺/多平台对比矩阵。
- 对 listing、主图、详情页、客服 FAQ、产品迭代的改进映射。
- 改进优先级和数据限制。
外部依赖
- 无必需外部依赖。
- Excel、CSV、JSON、DOCX 可由当前会话可用的文件读取能力处理。
- 自动采集评论需要用户确认平台授权、登录状态、平台条款和采集范围；默认不执行。
限制与已知问题
- 静态评论文本无法完全判断真实购买、刷评或恶意评价，只能给风险信号。
- 评论样本过少、时间跨度过短或只包含差评时，不能代表整体口碑。
- 百分比必须说明分母。
- 不展示完整个人信息，不把个体用户作为攻击对象。
- 不承诺改进建议一定提升评分、销量或转化。
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 · 56 lines · 35 tokens per session scan A 1735f5b86f6d
merch-review is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 987 once invoked, about $0.0002 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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