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Agent Skill · 研究检索

customer-feedback-analyzer(客户反馈分析仪)

customer-feedback-analyzer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

customer-feedback-analyzer 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,权限为权限未标注;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 29,103。

总安装 3,109周安装 127GitHub Stars 26下载 996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
MIT

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:customer-feedback-analyzer(客户反馈分析仪)
来源仓库:https://github.com/daffy0208/ai-dev-standards
仓库路径:skills/customer-feedback-analyzer
安装命令:
npx skills add https://github.com/daffy0208/ai-dev-standards --skill customer-feedback-analyzer
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/daffy0208/ai-dev-standards --skill customer-feedback-analyzer

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。

customer-feedback-analyzer 系统化分类与优先级反馈事项。整合调查、工单与访谈等多渠道输入源。支持闭环通知与团队洞察分享机制。customer-feedback-analyzer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。帮助平衡用户需求与产品战略方向。

适用于 SaaS 产品迭代与客户体验优化。

来源仓库内路径:skills/customer-feedback-analyzer

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/daffy0208/ai-dev-standards --skill customer-fe…;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

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

customer-feedback-analyzer 怎么安装?

在终端执行 npx skills add https://github.com/daffy0208/ai-dev-standards --skill customer-feedback-ana…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

customer-feedback-analyzer 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。

customer-feedback-analyzer 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

customer-feedback-analyzer 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

customer-feedback-analyzer 用的人多吗?

公开数据是累计安装 3,109 次,近一周安装 127 次,下载 996 次,GitHub 26 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。

customer-feedback-analyzer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
customer-feedback-analyzer

客户反馈分析仪 · 本页

3,10926权限未标注MIT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

Customer Feedback Analyzer

Collect, analyze, and prioritize user feedback to inform product decisions.

Core Principle

Never collect feedback you won't act on. Collecting feedback creates expectation of action. If you can't commit to reviewing and acting on it, don't ask for it. Destroys trust.

Feedback Channels

1. In-App Feedback Widget

Best for: Contextual feedback, low friction

// Contextual feedback
<FeedbackWidget
  context={{
    page: 'dashboard',
    feature: 'export',
    user_action: 'clicked_export'
  }}
  placeholder="How can we improve exports?"
/>

Pros: High quality (contextual), immediate Cons: Can interrupt user flow

2. NPS Surveys

Best for: Measuring overall satisfaction and loyalty

Question: "How likely are you to recommend [Product] to a friend or colleague?"
Scale: 0-10

Scoring:
  Promoters (9-10): Love your product, will advocate
  Passives (7-8): Satisfied but not enthusiastic
  Detractors (0-6): Unhappy, will churn

NPS = % Promoters - % Detractors

Benchmarks:
  Excellent: ≥50
  Good: 30-49
  Needs Work: <30

Follow-up question: "What's the main reason for your score?"

3. Support Tickets

Best for: Identifying recurring issues

Pattern Recognition:
  - Same issue reported 5+ times → UX problem, not edge case
  - Support time > 10 min per ticket → Needs better docs
  - Ticket volume spike → Recent deploy likely caused issue

4. User Interviews

Best for: Deep qualitative insights

Interview Structure:
  1. Background (5 min): Their role, use case
  2. Problem Discovery (10 min): Challenges they face
  3. Solution Validation (10 min): Show prototype, get reaction
  4. Wrap-up (5 min): Any other feedback?

Sample Size: 5-10 users per persona

5. Feature Request Voting

Best for: Prioritizing roadmap

Tools: Canny, ProductBoard, Upvoty

Benefits:
  - See most requested features
  - Reduce duplicate requests
  - Public roadmap transparency
  - Close the loop automatically

Avoid:
  - Building everything requested
  - Letting voters drive strategy

6. Exit Interviews

Best for: Understanding why users churn

Key Questions:
  - What made you decide to cancel?
  - What feature/change would have kept you?
  - What are you switching to?
  - What did we do well?

Feedback Categorization

By Type

Bug: Something broken
  - "Export fails with >100 rows"
  - Priority: Fix immediately

Feature Request: New capability
  - "Add Slack integration"
  - Priority: Vote/validate

Enhancement: Improve existing feature
  - "Export should include timestamps"
  - Priority: Nice to have

Usability: Confusing UX
  - "Can't find where to invite team members"
  - Priority: High (friction)

Performance: Speed issue
  - "Dashboard loads slowly"
  - Priority: Depends on impact

By Severity

Critical: Blocks core workflow
  - "Can't save projects"
  - Action: Hotfix immediately

High: Significant friction
  - "Onboarding confusing"
  - Action: Fix this sprint

Medium: Minor annoyance
  - "Button text unclear"
  - Action: Fix next quarter

Low: Edge case or cosmetic
  - "Spacing looks off on mobile"
  - Action: Backlog

By Frequency

Widespread: 50+ reports
  → High priority

Common: 10-50 reports
  → Medium priority

Occasional: 5-10 reports
  → Low priority, monitor

Rare: <5 reports
  → Likely edge case, document workaround

By User Segment

Power Users: High engagement, experienced
  → Actionable, technical feedback

New Users: Recently signed up
  → Onboarding issues, first impressions

Churned Users: Cancelled/inactive
  → Why did they leave?

Enterprise: Paying customers
  → Security, compliance, integrations

Prioritization Framework

Priority Score Formula

Score = Impact (1-5) × Frequency (1-5) × Strategic Alignment (1-5)

Score ≥ 40: High Priority (next sprint)
Score 20-39: Medium Priority (next quarter)
Score < 20: Low Priority (backlog or never)

Example:

Feedback: "Add Slack integration"
  Impact: 4 (significantly improves collaboration)
  Frequency: 5 (50+ requests)
  Strategic Alignment: 4 (fits roadmap)
  Score: 4 × 5 × 4 = 80

  Decision: HIGH PRIORITY - Build next sprint

Feedback: "Change button color"
  Impact: 1 (minor cosmetic)
  Frequency: 1 (1 person mentioned)
  Strategic Alignment: 1 (not strategic)
  Score: 1 × 1 × 1 = 1

  Decision: LOW PRIORITY - Backlog

Close the Feedback Loop

1. Acknowledge

Email Template:

Subject: Thanks for your feedback!

Hi [Name],

Thanks for taking the time to share your thoughts on [topic].

We review all feedback and use it to prioritize our roadmap. I've shared
your input with the product team.

You can track feature requests on our public roadmap: [link]

Thanks for helping us improve!
[Your Name]

2. Act

Decision Tree: Is it reported 10+ times?
  Yes → Add to roadmap
  No → Monitor

  Does it align with strategy?
  Yes → Prioritize
  No → Document why not

  Can we ship in 2 weeks?
  Yes → Quick win, do it
  No → Add to backlog

3. Notify Users Who Requested It

Email Template:

Subject: You asked for [Feature] - it's live!

Hi [Name],

Remember when you asked us to add [feature]? Good news - it's live!

[Screenshot/GIF of feature]

Here's how it works:

1. [Step 1]
2. [Step 2]

Try it now: [Link]

Thanks for the feedback that made this happen.
[Your Name]

P.S. Have more ideas? Reply to this email.

4. Public Changelog

## [Feature] is now live!

Requested by 47 users, [Feature] lets you [benefit].

How it works:

- [Key point 1]
- [Key point 2]

Try it: [Link]
Thanks to everyone who suggested this!

Common Feedback Patterns

Squeaky Wheel Syndrome

Problem: Vocal minority ≠ Real need

Example:
  - 1 user emails daily about dark mode
  - Analytics show 2% use dark mode

Action: Validate with data before building

Silent Churn

Problem: Users leave without complaining

Example:
  - Retention drops from 40% to 30%
  - No feedback, no complaints

Action:
  - Proactive exit interviews
  - Check analytics for drop-off points
  - Run usability tests

Feature Bloat Risk

Problem: Building everything requested leads to bloat

Example:
  - 'Add Excel export'
  - 'Add CSV export'
  - 'Add JSON export'
  - 'Add PDF export'

Action: Build generic solution, not every variant

Synthesis & Reporting

Weekly Feedback Summary

feedback_summary:
  period: "2024-01-15 to 2024-01-22"
  total_items: 87

  top_themes:
    - theme: "Slack Integration"
      frequency: 23
      severity: high
      example_quotes:
        - "We need Slack notifications"
        - "Can't notify team without Slack"
      recommended_action: "Build Slack integration next sprint"

    - theme: "Slow Dashboard Load"
      frequency: 15
      severity: medium
      example_quotes:
        - "Dashboard takes 10+ seconds"
        - "Performance is terrible"
      recommended_action: "Optimize queries, add caching"

    - theme: "Mobile App Request"
      frequency: 8
      severity: low
      example_quotes:
        - "I want a mobile app"
      recommended_action: "Monitor, not enough demand yet"

  nps:
    score: 42
    detractor_reasons:
      - "Too expensive" (12 mentions)
      - "Missing features" (8 mentions)
      - "Slow performance" (5 mentions)

  prioritized_backlog:
    - feedback: "Add Slack integration"
      score: 80
      priority: high

    - feedback: "Optimize dashboard performance"
      score: 45
      priority: medium

    - feedback: "Mobile app"
      score: 16
      priority: low

Tools & Software

Feedback Collection:
  - In-app: Canny, UserVoice, Intercom
  - Surveys: Typeform, SurveyMonkey, Delighted (NPS)
  - User Research: Calendly, Zoom, UserTesting.com

Analysis:
  - Qualitative: Dovetail, Notion, Airtable
  - Quantitative: Excel, Google Sheets, Tableau
  - Sentiment: MonkeyLearn, Lexalytics

Roadmap Transparency:
  - Public Roadmap: Canny, ProductBoard, Trello
  - Changelog: Headway, ReleaseNotes.io, Beamer

Feedback Cadence

Daily:
  - Review support tickets
  - Monitor in-app feedback

Weekly:
  - Synthesize themes
  - Share with product team
  - Prioritize top requests

Monthly:
  - Send NPS survey
  - Review feature requests
  - Update public roadmap

Quarterly:
  - User interviews (5-10)
  - Exit surveys for churned users
  - Competitive feedback analysis

Quick Start Checklist

  • Set up in-app feedback widget
  • Schedule NPS survey (monthly)
  • Create feedback tracking spreadsheet
  • Review support tickets weekly
  • Conduct 2-3 user interviews
  • Set up public roadmap (optional)
  • Create email templates for acknowledging feedback
  • Document feedback categorization process

Common Pitfalls

❌ Collecting feedback without acting: Damages trust ❌ Building everything requested: Feature bloat ❌ Not validating with data: Vocal minority ≠ majority ❌ Ignoring silent majority: Not everyone gives feedback ❌ No follow-up: Users want to know you listened

Summary

Great feedback analysis:

  • ✅ Multiple channels (surveys, tickets, interviews)
  • ✅ Categorize and prioritize systematically
  • ✅ Act on high-priority feedback quickly
  • ✅ Close the loop (notify users)
  • ✅ Balance requests with strategy
  • ✅ Share insights with team

平台分布

Claude Code

26.07%
按下载量换算260

OpenCode

25.63%
按下载量换算255

Gemini CLI

16.01%
按下载量换算159

Antigravity

12.76%
按下载量换算127

Codex

7.36%
按下载量换算73

Cursor

3.52%
按下载量换算35

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。

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