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Agent Skill · 研究检索

review-audit(审查审核)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

review-audit 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 26,422。

总安装 3,574周安装 146GitHub Stars 88下载 1,145核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:review-audit(审查审核)
来源仓库:https://github.com/motion-team/creative-strategy-skills
仓库路径:skills/review-audit
安装命令:
npx skills add https://github.com/motion-team/creative-strategy-skills --skill review-audit
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/motion-team/creative-strategy-skills --skill review-audit

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

review-audit 用于安全审计、权限检查和常见漏洞排查辅助。适合梳理敏感配置、分析鉴权逻辑或生成安全复核清单。通过 npx 命令从 GitHub 仓库安装并使用该技能。使用时不能直接采信输出结果,尤其涉及密钥或生产系统时应谨慎。建议先确认最小权限、脱敏方式及操作边界后再执行相关任务。

来源仓库内路径:skills/review-audit

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/motion-team/creative-strategy-skills --skill r…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

review-audit 怎么安装?

在终端执行 npx skills add https://github.com/motion-team/creative-strategy-skills --skill review-audi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

review-audit 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

review-audit 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

review-audit 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

review-audit 用的人多吗?

公开数据是累计安装 3,574 次,近一周安装 146 次,下载 1,145 次,GitHub 88 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

review-audit 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
review-audit

审查审核 · 本页

3,57488只读未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Review Audit

This system mines positive customer reviews to extract the insights that make ad copy actually work — the real language, real pain, real moments, and real transformations that customers experienced. The output feeds directly into creative strategy and hook writing.

The goal is not to summarize reviews. The goal is to find the raw material for ads.


What You Need Before Starting

Reviews can be provided in any format:

  • Pasted directly into the chat
  • CSV or spreadsheet upload
  • Copy/pasted from a document

If multiple products are present in the review set, identify them before beginning. All output is separated by product.

If the format is unclear or product attribution is ambiguous, ask before proceeding.


Step 1: Group by Product

If the brand sells multiple products, sort all reviews by product first. Every subsequent step runs separately per product.

If all reviews are for a single product, skip grouping and proceed.


Step 2: Score Review Quality (1–5)

Before analysis, score every review for quality. This determines what gets analyzed and what gets discarded.

ScoreWhat it looks like
1Garbage — gibberish, swear words, 2–3 meaningless words, zero signal ("great product", "love it", "👍")
2Low signal — very short, vague, no specific detail or emotion
3Moderate — mentions the product, some specificity, but no vivid detail or emotional depth
4High quality — specific, describes a real experience, references a before/after or a feeling
5Gold — long, emotional, vivid, paragraph-level detail; the customer was so moved they wrote an essay about it

Score 5 reviews are the priority. They contain the most usable language and the deepest insight.


Step 3: Filter

Discard all reviews scored 1. Do not include them in analysis.

Analyze scores 2–5, with emphasis on 4s and 5s. Low-scoring reviews (2–3) can contribute to pattern identification but should not be the source of pulled quotes.


Step 4: Extract Insights by Bucket

Run this analysis separately for each product. Within each bucket, group similar insights together and write a brief summary of the pattern. Then pull the best word-for-word quotes that exemplify it.

Do not editorialize the quotes. Pull them exactly as written.


Bucket 1: Pain Points

*What problem were they experiencing before they found this product?*

Look for: descriptions of the problem they had, how long they'd had it, what they'd tried before, how it affected their life, the emotional weight of living with it.

For each pain theme identified:

  • Name the theme
  • Write a 2–3 sentence summary of the pattern across reviews
  • Flag the strongest quotes for the swipe file

Bucket 2: Trigger Moments

*What finally made them buy?*

Look for: the specific moment, event, or realization that pushed them over the edge. This is the thing that turned a maybe into an add-to-cart. It could be a life event (wedding, diagnosis, vacation), a recommendation (friend, doctor, TikTok), hitting a breaking point, or running out of patience with other solutions.

For each trigger theme identified:

  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file

Bucket 3: Objections Before Purchasing

*What almost stopped them from buying?*

Look for: skepticism they mention having had, comparisons to other products they'd tried, price hesitation, disbelief that this would actually work, fear of wasting money again.

Note: In positive reviews, objections are almost always mentioned in past tense — "I was skeptical but..." or "I almost didn't try it because..." These are gold for objection-handling ad copy.

For each objection theme identified:

  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file

Bucket 4: Transformations

*What changed for them after using the product?*

Look for: the specific result they experienced, how their life is different now, the emotional shift (confidence, relief, freedom, pride), and — most importantly — how they describe the transformation in their own words. The more specific and visceral, the better.

For each transformation theme identified:

  • Name the theme
  • Write a 2–3 sentence summary of the pattern
  • Flag the strongest quotes for the swipe file

Bucket 5: Standout Language & Ad-Ready Phrases

*Exact language worth stealing for ads.*

This bucket is different from the others. It is not organized by theme — it is a curated collection of the most vivid, emotionally charged, specific, and scroll-stopping phrases pulled from across all buckets. These are the lines that made you stop while reading. The ones that don't need to be rewritten. The ones a copywriter would highlight and build an ad around.

Pull these verbatim. Note which product they're from.

What to look for:

  • Unusually specific descriptions of pain or transformation
  • Phrases that capture an emotion in a way you couldn't have written yourself
  • Before/after language that is visceral and concrete
  • Lines that could work as a hook with zero editing
  • Anything that made you feel something while reading it

Output Format

Produce a separate full output for each product. Structure:

─────────────────────────────────────
PRODUCT: [Product Name]
Reviews analyzed: [X] | Discarded (score 1): [X]
─────────────────────────────────────

BUCKET 1: PAIN POINTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 2: TRIGGER MOMENTS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 3: OBJECTIONS BEFORE PURCHASING

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 4: TRANSFORMATIONS

[Theme Name]
Summary: [2–3 sentences on the pattern]

---

BUCKET 5: STANDOUT LANGUAGE & AD-READY PHRASES

"[Exact quote]"
"[Exact quote]"
"[Exact quote]"
[etc.]

─────────────────────────────────────

All word-for-word quotes are collected in Bucket 5. Do not scatter quotes throughout buckets 1–4 — keep the summaries clean and let the swipe file be the dedicated place for raw language.


How This Feeds the Rest of the Stack

The output of this analysis plugs directly into creative strategy and execution:

  • Pain Points → Creative Strategy Engine — pain buckets map directly to the pain/desire anchor layer
  • Trigger Moments → Hook Writing — trigger moments are often the most powerful hook material; they capture the exact moment of emotional readiness
  • Objections → Hook Writing / Creative Mechanics — objections inform Borrowed Enemy, Reframe, and Risk Reversal mechanics and hook tactics
  • Transformations → Hook Writing — transformation language feeds aspirational and social proof hooks
  • Standout Language → Hook Voice Patterns — the best phrases can be added directly to the swipe file as native voice patterns pulled from real customers

Notes on Quality

  • Score 5 reviews should be read in full and treated as primary sources
  • Score 2–3 reviews are useful for pattern confirmation but not quote sourcing
  • If the review set is small (under 20 reviews), note this — patterns may not be statistically meaningful but language is still usable
  • If a product has too few quality reviews to surface meaningful patterns, flag it rather than manufacturing themes that aren't there

平台分布

Codex

35.9%
按下载量换算411

Claude

30.13%
按下载量换算345

Cursor

20.36%
按下载量换算233

Gemini CLI

9.73%
按下载量换算111

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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