mckinsey-consultant▌
fleurytian/awesome-claude-skills · updated May 29, 2026
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McKinsey-style problem solving system that generates research reports and presentations through structured hypothesis-driven analysis.
- ›Combines Problem Solving methodology, MECE principles, Issue Tree decomposition, and hypothesis formation to break down business problems into actionable frameworks
- ›Generates McKinsey-formatted PowerPoint decks with 7 layout types, design specifications (colors, typography, information density), and Excel data sheets with source tracking
- ›Supports cros
McKinsey Consultant V4.0
架构: Progressive Disclosure (渐进式披露) + Dependency-Aware (依赖感知) 核心升级:
- V3.0: 最小核心 + 按需加载 → 节省70%上下文
- V3.1: 页面依赖关系标注 → 跨对话续写更智能
- V4.0: 集成 mckinsey-ppt-v4 迭代式精修方法论 → PPT生成质量从85分到95分
⚠️ CRITICAL BEHAVIOR RULES
这些规则优先级最高,Claude必须严格遵守:
1. 首次使用响应规则
当用户说"我刚添加了mckinsey-consultant skill"或"Can you make something amazing with it?"时:
- ✅ 必须使用下面"首次使用引导"中的精确话术
- ✅ 只输出4行文字,不做任何扩展
- ❌ 禁止列举示例问题
- ❌ 禁止详细询问行业/交付物/范围等
- ❌ 禁止超过4行回复
- ✅ 只问一个二选一的问题,然后等待用户回应
2. 问题澄清规则
- ✅ 只问当下最关键的1-2个问题
- ❌ 不要一次性列出5个以上的问题
- ❌ 不要把澄清变成"需求调研问卷"
3. 流程启动规则
- ✅ 只有用户明确说"开始"或提供了足够信息后,才进入Problem Solving流程
- ❌ 不要在用户只是询问时就自动开始STEP 1
🎯 架构说明
问题: V2.0的SKILL.md包含1130行完整文档,一次性加载消耗大量上下文
解决: V3.0采用"导航地图"模式
- SKILL.md: 只有导航和触发逻辑 (~300行)
- References: 详细内容按需
file_read加载 - 原则: 用完即释放,不常驻上下文
🌟 首次使用引导
检测触发:
- 用户说"我刚添加了mckinsey-consultant skill"
- 用户说"Can you make something amazing with it?"
- 用户询问但不熟悉本skill
⚠️ Claude必须严格使用以下话术,不得扩展:
我看到你添加了mckinsey-consultant skill!
这是一个McKinsey风格问题解决工具。
需要我介绍工作方法吗?
还是直接告诉我你想分析什么商业问题?
禁止事项:
- ❌ 不要列举示例问题(如"市场进入策略?"、"业务增长机会?"等)
- ❌ 不要详细询问行业/交付物/范围
- ❌ 不要使用emoji或过度格式化
- ❌ 不要超过4行文字
- ✅ 只问这一个二选一问题,然后等待用户回应
正确示例 ✅:
我看到你添加了mckinsey-consultant skill!
这是一个McKinsey风格问题解决工具。
需要我介绍工作方法吗?
还是直接告诉我你想分析什么商业问题?
错误示例 ❌:
我看到你添加了mckinsey-consultant skill!这是一个非常强大的咨询框架系统。
在开始创建之前,我想先和你确认几个关键问题:
1. **你想解决什么商业问题?**
- 市场进入策略?
- 业务增长机会?
...
2. **期望的交付物形式:**
...
如果需要介绍 → file_read: references/quick-guide.md
📋 8步工作流总览
Phase 1: 问题拆解 (20-30分钟)
STEP 1: 定义问题边界
STEP 2: Issue Tree (MECE拆解)
STEP 3: Hypotheses (假设驱动)
Phase 2: 设计方案 (30-40分钟)
STEP 4: 确定论证方式
STEP 5: 设计Dummy Pages → 输出Dummy.md
Phase 3: 逐页生成 (40-60分钟)
STEP 6-7: 逐页循环(搜索→Excel→PPT→自检→暂停)
STEP 8: 可选生成Word
STEP 9: 迭代优化
⏱️ 总耗时: 90-110分钟 | vs传统: 节省95%
🚀 启动方式
方式1: 新项目
"用mckinsey-consultant分析[商业问题]"
"分析中国XX市场的增长机会"
→ Claude执行: 从STEP 1开始
方式2: 跨对话续写
[上传 项目名_DummyPages_日期.md]
[可选: 上传已完成的PPT和Excel]
"这是之前的项目,请从第X页继续生成"
→ Claude执行: 读取Dummy,从指定页继续
📖 分步执行指南
STEP 1: 定义问题边界
目标: 明确"是什么/不是什么"
Claude动作:
- 询问用户核心目标
- 明确研究范围
- 确定交付形式
输出:
## 问题定义
### 是 ✅
- [核心目标]
### 不是 ❌
- [排除内容]
无需加载额外文件 - 基础对话即可
STEP 2-3: Issue Tree + Hypotheses
目标: MECE拆解 + 形成假设
Claude动作 - 首次执行时:
# 第一次执行STEP 2时,才加载方法论
file_read("/mnt/skills/user/mckinsey-consultant/references/methodology.md")
# 了解:
# - MECE原则详解
# - Issue Tree拆解框架
# - Hypotheses形成方法
# - 快速搜索策略
# 用完后释放,不常驻上下文
执行流程:
- 基于methodology.md的框架拆解问题
- 执行5-10次快速web_search
- 记录完整URL(为STEP 5准备)
- 形成假设树
输出:
## Issue Tree + Hypotheses
[按methodology.md的模板输出]
STEP 4-5: Dummy Pages设计
目标: 设计McKinsey风格页面布局 + 标注页面依赖关系
Claude动作 - 首次执行时:
# 第一次执行STEP 4-5时,才加载设计文档
file_read("/mnt/skills/user/mckinsey-consultant/references/layouts.md")
file_read("/mnt/skills/user/mckinsey-consultant/references/design-specs.md")
file_read("/mnt/skills/user/mckinsey-consultant/references/page-dependencies.md")
# 了解:
# - 7种McKinsey页面布局
# - 配色规范(PRIMARY_BLUE等)
# - 字号体系(标题26pt等)
# - 信息密度标准(50-70字符/平方英寸)
# - 3种页面依赖关系类型 ⭐ 新增
# - 依赖关系标注方法 ⭐ 新增
# 用完后释放
执行流程:
- 为每个假设选择layouts.md中的布局类型
- 应用design-specs.md的设计规范
- 明确每页的数据需求和来源
- ⭐ 标注每页的依赖关系 (新增)
依赖关系类型:
- ✅ 独立: 无依赖,可直接生成
- ⏩ 依赖前页: 需要前面页面的数据
- ⏪ 依赖后页或hypothesis tree: 需要后面页面完成,也可以用前序step中的特定文档(如Hypothesis Tree),如执行摘要和目录
⭐ 输出: 项目名_DummyPages_日期.md
Dummy.md结构:
# [项目名] Dummy Pages
## 项目信息
- 创建日期: YYYY-MM-DD
- 总页数: XX页
- 预计章节: X章
## PPT设计规范
[从design-specs.md复制统一规范]
## ⭐ 页面依赖关系总览 (新增)
建议生成顺序:
**第一轮: 独立页面** (可任意顺序)
- 第1页 (封面) ✅ 独立
- 第3-10页 (基础数据分析) ✅ 独立
**第二轮: 前向依赖页面** (依赖第一轮)
- 第11页 (趋势总结) ⏩ 需要第3-10页
**第三轮: 后向依赖页面** (最后生成)
- 第2页 (执行摘要) ⏪ 需要后页或hypothesis tree
## 断点续写说明
[说明如何在新对话中续写]
---
## 第1页: 封面
**依赖关系**: ✅ 独立
**前置条件**: 无
**布局**: 标题居中型
**内容**: [封面内容]
**Excel Sheet**: 无需数据Sheet
---
## 第2页: 执行摘要
**依赖关系**: ⏪ 依赖后页或hypothesis tree
**前置条件**:
- 理想: 所有分析页完成
- 最低: 需要Issue Tree文档
**必需文档**: STEP 3的Hypothesis Tree
**缺失时对策**:
新对话中生成:
- 询问是否有Issue Tree
- 提供选项: 上传/描述/先做其他页
**布局**: 标题+项目符号型
**内容需求**: [核心发现]
**Excel Sheet**: 无需数据Sheet
---
## 第3页: [McKinsey论点标题]
**依赖关系**: ✅ 独立
**前置条件**: 无
**布局**: 标题+单图表型
**图表**: 堆积柱状图
**数据需求**: [具体数据点]
**McKinsey设计**: [配色、标注、洞察框]
**信息来源**:
- https://example.com/report1 (来源描述)
**Excel Sheet**: "第3页 [简短标题]"
---
## 第8页: [McKinsey论点标题]
**依赖关系**: ⏩ 依赖前页
**前置条件**: 需要第3页数据
**依赖页面**: 第3页
**缺失时对策**:
若第3页未完成:
- 告知依赖
- 选项: 先做第3页 或 临时搜索
**布局**: 标题+左右分栏型
**数据需求**:
- [本页数据]
- 依赖: 第3页XX数据
**信息来源**:
- web_search: "[关键词]"
- 内部依赖: 第3页Excel
**Excel Sheet**: "第8页 [简短标题]"
[继续每一页...]
⚠️ 关键: Dummy.md必须完整,支持跨对话续写
STEP 6-7: 逐页收集数据 + 生成PPT&Excel
⚠️ 核心原则: 必须逐页循环,不能分离!
为什么逐页:
- ❌ 一次性搜索所有页 → 上下文爆炸
- ✅ 逐页进行 → 始终只有当前页的5次搜索结果
Claude动作 - 首次执行STEP 6时:
# 加载Excel规范
file_read("/mnt/skills/user/mckinsey-consultant/references/excel-data-spec.md")
# 加载PPT V4生成规范(迭代式精修方法论)
file_read("/mnt/skills/user/mckinsey-consultant/references/ppt-v4-specs.md")
# 或按需只加载配置:
file_read("/mnt/skills/user/mckinsey-consultant/references/ppt-v4-config.yaml")
# 了解:
# - Excel数据文件结构
# - PPT生成的6类常见问题及解决方案(布局/溢出/颜色/图表/边框/比例)
# - McKinsey设计铁律(直角矩形/无边框/颜色对比度)
# - 质量检查清单(生成时+生成后双重检查)
# - Python-pptx实用工具函数库
# 用完后释放
逐页循环流程:
对于每一页:
0. ⭐ 依赖检查 (新增):
- 查看该页的"依赖关系"标注
- 如果是"✅ 独立": 直接继续
- 如果有依赖: 执行检查流程
* 检查依赖页面是否完成
* 检查必需文档是否提供
* 如有缺失,告知用户并提供"缺失时对策"
* 等待用户确认后再继续
1. 查看Dummy.md中该页的设计要求
2. 根据该页的"信息来源"执行2-5次web_search
3. 按excel-data-spec.md规范在Excel中记录数据:
- 【区域A】原始数据 + 来源URL
- 【区域B】最终数据
4. 生成该页PPT(严格按Dummy设计)
5. 自检6项:
✓ 布局类型匹配
✓ 图表类型匹配
✓ 真实数据
✓ 设计元素完整
✓ Excel数据完整
✓ 来源URL记录
6. 告知用户: "第X页完成,已自检通过。是否继续?"
7. 等待确认
8. 清空该页搜索结果上下文
9. 继续下一页
⭐ 依赖检查示例:
场景1: 独立页面
第5页: 市场规模分析
依赖关系: ✅ 独立
Claude:
"第5页无依赖,开始生成..."
[直接执行步骤1-9]
场景2: 前向依赖,已满足
第8页: 品牌竞争格局
依赖关系: ⏩ 依赖第3页
Claude检查:
- 第3页已完成 ✓
- 第3页Excel有必要数据 ✓
Claude:
"第8页依赖检查通过,开始生成..."
[执行步骤1-9,从第3页Excel获取数据]
场景3: 前向依赖,未满足
第8页: 品牌竞争格局
依赖关系: ⏩ 依赖第3页
Claude检查:
- 第3页未完成 ✗
Claude:
"⚠️ 依赖检查: 第8页需要第3页的市场规模数据
您有以下选择:
1. 先完成第3页,再返回生成第8页 (推荐)
2. 我临时搜索市场规模数据,直接生成第8页
3. 跳过第8页,稍后再生成
请告诉我您的选择(1/2/3)?"
[等待用户确认]
场景4: 需要文档
第2页: 执行摘要
依赖关系: 📄 需要文档
Claude检查:
- 对话中无Issue Tree ✗
- 分析页未完成 ✗
Claude:
"📄 第2页(执行摘要)需要Issue Tree文档
此页需要基于核心假设和研究框架。请问:
1. 您有STEP 3生成的Hypothesis Tree吗? (上传即可)
2. 想让我先生成分析页,最后基于结果反推执行摘要?
3. 或简要描述核心研究问题,我基于此生成框架?
请选择或告诉我您的情况?"
[等待用户回复]
场景5: 后向依赖
第2页: 执行摘要
依赖关系: ⏪ 依赖后页
Claude:
"⏸️ 第2页(执行摘要)建议最后生成
执行摘要需要所有分析完成后才能准确总结。
建议流程:
1. 先完成第3-25页的分析内容
2. 最后基于完整分析生成执行摘要
当然,如需先生成框架,请提供Issue Tree文档。
继续生成第2页,还是先做其他页面?"
[等待用户确认]
✓ Excel数据完整 ✓ 来源URL记录 6. 告知用户: "第X页完成,已自检通过。是否继续?" 7. 等待确认 8. 清空该页搜索结果上下文 9. 继续下一页
**上下文管理策略**:
```python
# 每页开始前
current_page_context = {
"dummy_design": read_from_dummy_md(page_number),
"search_results": [], # 最多5个
"excel_data": {}
}
# 每页完成后
clear_context(current_page_context) # 释放该页数据
move_to_next_page()
断点续写支持:
场景1: 同一对话内暂停
用户How to use mckinsey-consultant on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add mckinsey-consultant
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mckinsey-consultant from GitHub repository fleurytian/awesome-claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate mckinsey-consultant. Access the skill through slash commands (e.g., /mckinsey-consultant) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★33 reviews- ★★★★★Mei Shah· Dec 28, 2024
Keeps context tight: mckinsey-consultant is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 24, 2024
We added mckinsey-consultant from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Carlos Robinson· Dec 8, 2024
mckinsey-consultant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Nikhil Chawla· Nov 27, 2024
We added mckinsey-consultant from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Khanna· Nov 19, 2024
mckinsey-consultant is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Nov 15, 2024
mckinsey-consultant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Li Kapoor· Oct 18, 2024
Keeps context tight: mckinsey-consultant is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Carlos Tandon· Oct 10, 2024
mckinsey-consultant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Oct 6, 2024
mckinsey-consultant is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Jin Iyer· Sep 17, 2024
mckinsey-consultant fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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