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Agent Skill · 研究检索

product-manager-toolkit(产品经理工具包)

product-manager-toolkit 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。

product-manager-toolkit 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 71,089。

总安装 196周安装 8GitHub Stars 50下载 63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:product-manager-toolkit(产品经理工具包)
来源仓库:https://github.com/hainamchung/agent-assistant
仓库路径:skills/product-manager-toolkit
安装命令:
npx skills add https://github.com/hainamchung/agent-assistant --skill product-manager-toolkit
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/hainamchung/agent-assistant --skill product-manager-toolkit

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。

用于查找、检索和筛选相关信息。适合根据关键词或任务场景快速定位候选结果。安装前建议确认权限范围和维护状态,避免触发联网或命令执行。

安装方式:通过 GitHub 仓库安装,命令为 npx skills add https://github.com/hainamchung/agent-assistant --skill product-manager-toolkit。支持 Codex、Claude、Cursor、Gemini CLI 等宿主环境。

来源仓库内路径:skills/product-manager-toolkit

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/hainamchung/agent-assistant --skill product-ma…;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

product-manager-toolkit 怎么安装?

在终端执行 npx skills add https://github.com/hainamchung/agent-assistant --skill product-manager-tool…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

product-manager-toolkit 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。

product-manager-toolkit 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。

product-manager-toolkit 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。

product-manager-toolkit 用的人多吗?

公开数据是累计安装 196 次,近一周安装 8 次,下载 63 次,GitHub 50 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。

product-manager-toolkit 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 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
product-manager-toolkit

产品经理工具包 · 本页

19650只读未明确通过

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

Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

Quick Start

For Feature Prioritization

python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

For Interview Analysis

python scripts/customer_interview_analyzer.py interview_transcript.txt

For PRD Creation

  1. Choose template from references/prd_templates.md
  2. Fill in sections based on discovery work
  3. Review with stakeholders
  4. Version control in your PM tool

Core Workflows

Feature Prioritization Process

  1. Gather Feature Requests

- Customer feedback - Sales requests - Technical debt - Strategic initiatives

  1. Score with RICE # Create CSV with: name,reach,impact,confidence,effort python scripts/rice_prioritizer.py features.csv

- Reach: Users affected per quarter - Impact: massive/high/medium/low/minimal - Confidence: high/medium/low - Effort: xl/l/m/s/xs (person-months)

  1. Analyze Portfolio

- Review quick wins vs big bets - Check effort distribution - Validate against strategy

  1. Generate Roadmap

- Quarterly capacity planning - Dependency mapping - Stakeholder alignment

Customer Discovery Process

  1. Conduct Interviews

- Use semi-structured format - Focus on problems, not solutions - Record with permission

  1. Analyze Insights python scripts/customer_interview_analyzer.py transcript.txt Extracts:

- Pain points with severity - Feature requests with priority - Jobs to be done - Sentiment analysis - Key themes and quotes

  1. Synthesize Findings

- Group similar pain points - Identify patterns across interviews - Map to opportunity areas

  1. Validate Solutions

- Create solution hypotheses - Test with prototypes - Measure actual vs expected behavior

PRD Development Process

  1. Choose Template

- Standard PRD: Complex features (6-8 weeks) - One-Page PRD: Simple features (2-4 weeks) - Feature Brief: Exploration phase (1 week) - Agile Epic: Sprint-based delivery

  1. Structure Content

- Problem → Solution → Success Metrics - Always include out-of-scope - Clear acceptance criteria

  1. Collaborate

- Engineering for feasibility - Design for experience - Sales for market validation - Support for operational impact

Key Scripts

rice_prioritizer.py

Advanced RICE framework implementation with portfolio analysis.

Features:

  • RICE score calculation
  • Portfolio balance analysis (quick wins vs big bets)
  • Quarterly roadmap generation
  • Team capacity planning
  • Multiple output formats (text/json/csv)

Usage Examples:

# Basic prioritization
python scripts/rice_prioritizer.py features.csv

# With custom team capacity (person-months per quarter)
python scripts/rice_prioritizer.py features.csv --capacity 20

# Output as JSON for integration
python scripts/rice_prioritizer.py features.csv --output json

customer_interview_analyzer.py

NLP-based interview analysis for extracting actionable insights.

Capabilities:

  • Pain point extraction with severity assessment
  • Feature request identification and classification
  • Jobs-to-be-done pattern recognition
  • Sentiment analysis
  • Theme extraction
  • Competitor mentions
  • Key quotes identification

Usage Examples:

# Analyze single interview
python scripts/customer_interview_analyzer.py interview.txt

# Output as JSON for aggregation
python scripts/customer_interview_analyzer.py interview.txt json

Reference Documents

prd_templates.md

Multiple PRD formats for different contexts:

  1. Standard PRD Template

- Comprehensive 11-section format - Best for major features - Includes technical specs

  1. One-Page PRD

- Concise format for quick alignment - Focus on problem/solution/metrics - Good for smaller features

  1. Agile Epic Template

- Sprint-based delivery - User story mapping - Acceptance criteria focus

  1. Feature Brief

- Lightweight exploration - Hypothesis-driven - Pre-PRD phase

Prioritization Frameworks

RICE Framework

Score = (Reach × Impact × Confidence) / Effort

Reach: # of users/quarter
Impact:
  - Massive = 3x
  - High = 2x
  - Medium = 1x
  - Low = 0.5x
  - Minimal = 0.25x
Confidence:
  - High = 100%
  - Medium = 80%
  - Low = 50%
Effort: Person-months

Value vs Effort Matrix

         Low Effort    High Effort

High     QUICK WINS    BIG BETS
Value    [Prioritize]   [Strategic]

Low      FILL-INS      TIME SINKS
Value    [Maybe]       [Avoid]

MoSCoW Method

  • Must Have: Critical for launch
  • Should Have: Important but not critical
  • Could Have: Nice to have
  • Won't Have: Out of scope

Discovery Frameworks

Customer Interview Guide

1. Context Questions (5 min)
   - Role and responsibilities
   - Current workflow
   - Tools used

2. Problem Exploration (15 min)
   - Pain points
   - Frequency and impact
   - Current workarounds

3. Solution Validation (10 min)
   - Reaction to concepts
   - Value perception
   - Willingness to pay

4. Wrap-up (5 min)
   - Other thoughts
   - Referrals
   - Follow-up permission

Hypothesis Template

We believe that [building this feature]
For [these users]
Will [achieve this outcome]
We'll know we're right when [metric]

Opportunity Solution Tree

Outcome
├── Opportunity 1
│   ├── Solution A
│   └── Solution B
└── Opportunity 2
    ├── Solution C
    └── Solution D

Metrics & Analytics

North Star Metric Framework

  1. Identify Core Value: What's the #1 value to users?
  2. Make it Measurable: Quantifiable and trackable
  3. Ensure It's Actionable: Teams can influence it
  4. Check Leading Indicator: Predicts business success

Funnel Analysis Template

Acquisition → Activation → Retention → Revenue → Referral

Key Metrics:
- Conversion rate at each step
- Drop-off points
- Time between steps
- Cohort variations

Feature Success Metrics

  • Adoption: % of users using feature
  • Frequency: Usage per user per time period
  • Depth: % of feature capability used
  • Retention: Continued usage over time
  • Satisfaction: NPS/CSAT for feature

Best Practices

Writing Great PRDs

  1. Start with the problem, not solution
  2. Include clear success metrics upfront
  3. Explicitly state what's out of scope
  4. Use visuals (wireframes, flows)
  5. Keep technical details in appendix
  6. Version control changes

Effective Prioritization

  1. Mix quick wins with strategic bets
  2. Consider opportunity cost
  3. Account for dependencies
  4. Buffer for unexpected work (20%)
  5. Revisit quarterly
  6. Communicate decisions clearly

Customer Discovery Tips

  1. Ask "why" 5 times
  2. Focus on past behavior, not future intentions
  3. Avoid leading questions
  4. Interview in their environment
  5. Look for emotional reactions
  6. Validate with data

Stakeholder Management

  1. Identify RACI for decisions
  2. Regular async updates
  3. Demo over documentation
  4. Address concerns early
  5. Celebrate wins publicly
  6. Learn from failures openly

Common Pitfalls to Avoid

  1. Solution-First Thinking: Jumping to features before understanding problems
  2. Analysis Paralysis: Over-researching without shipping
  3. Feature Factory: Shipping features without measuring impact
  4. Ignoring Technical Debt: Not allocating time for platform health
  5. Stakeholder Surprise: Not communicating early and often
  6. Metric Theater: Optimizing vanity metrics over real value

Integration Points

This toolkit integrates with:

  • Analytics: Amplitude, Mixpanel, Google Analytics
  • Roadmapping: ProductBoard, Aha!, Roadmunk
  • Design: Figma, Sketch, Miro
  • Development: Jira, Linear, GitHub
  • Research: Dovetail, UserVoice, Pendo
  • Communication: Slack, Notion, Confluence

Quick Commands Cheat Sheet

# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

# Create sample data
python scripts/rice_prioritizer.py sample

# JSON outputs for integration
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

平台分布

Codex

34.58%
按下载量换算22

Claude

32.86%
按下载量换算21

Cursor

20.37%
按下载量换算13

Gemini CLI

8.64%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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