An AI-driven strategic consulting knowledge base equipping agents with McKinsey's core methodologies, thinking models, and comprehensive industry insights.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mckinsey-100y-knowledge-base
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mckinsey-100y-knowledge-base using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is McKinsey 100-Year Knowledge Base?
The McKinsey 100-Year Knowledge Base is a comprehensive repository of elite management consulting frameworks, structured thinking tools, and curated global industry intelligence. Consolidating 191 high-value resources, this skill brings the legendary consulting expertise of McKinsey directly to your AI agent. By using this asset among your Openclaw Skills, you can empower your digital assistants to decompose complex corporate problems, draft structured communications using the Pyramid Principle, and perform market analysis across sectors like AI, Finance, Energy, and Automotive.
The skill utilizes a Progressive Disclosure and Knowledge Catalog architecture, allowing agents to dynamically query specific modules as needed. This prevents context window bloat while ensuring deep, context-aware assistance. Combining century-old strategic methodologies with real-time IMA API database queries, it is the ultimate tool for strategic planning, professional writing, and advanced business analysis.
McKinsey 100-Year Knowledge Base Use Cases
- Strategic Problem Solving: Break down complex corporate challenges using the MECE principle, Issue Trees, and the 4S (State, Structure, Solve, Sell) framework.
- Executive Writing & Communication: Structure presentations, emails, and reports according to the Pyramid Principle for clear, high-impact logical flow.
- Market & Trend Research: Retrieve insights from McKinsey's global industry whitepapers, covering generative AI economy, EV transitions, financial wealth management, retirement trends.
- Decision-Making Frameworks: Access over 100 structured thinking models (including strategic analysis, innovation, and system thinking) to guide business decisions.
- Professional Upskilling: Apply McKinsey work methods, fast-reading techniques, and structured interrogation strategies to day-to-day operations.
How McKinsey 100-Year Knowledge Base Works
- Trigger Activation: The AI agent detects relevant keywords such as MECE, Pyramid Principle, McKinsey methodology, or strategic analysis to activate the skill.
- Progressive Module Loading: Depending on the specific user request, the agent selectively loads the appropriate markdown reference (Core Methodology, Work Skills, Industry Reports, or Thinking Models).
- Real-time IMA API Query: For detailed or updated knowledge, the skill makes API requests to search the McKinsey 100Y or Thinking Models knowledge bases hosted on the IMA network.
- Structured Synthesis: The agent combines the retrieved framework with the user's business context, rendering a highly structured, strategic response.
- Collaborative Execution: If active, the skill seamlessly coordinates with the mckinsey-consultant skill to execute end-to-end consulting workflows.
McKinsey 100-Year Knowledge Base Setup
To install and configure the McKinsey 100-Year Knowledge Base skill in your local workspace, configure your IMA API credentials and point to the skill's reference files.
- Configure your IMA API credentials. Retrieve your Client ID and API Key, then store them in your home directory:
mkdir -p ~/.config/ima
echo "your_client_id" > ~/.config/ima/client_id
echo "your_api_key" > ~/.config/ima/api_key
- Verify the Node.js API client setup and run a test query against the McKinsey 100-Year Knowledge Base using the following command structure:
# Search the McKinsey 100-Year Knowledge Base
node /path/to/skills/mckinsey-100y-knowledge-base/ima_api.cjs "openapi/wiki/v1/search_knowledge" '{"knowledge_base_id": "lEanMn3jC4JjNrNulwh5-uoANKemo_efKzqpqdZzG7k=", "query": "AI", "cursor": ""}' '{"clientId":"YOUR_CLIENT_ID","apiKey":"YOUR_API_KEY"}'
# Browse content inside the McKinsey Thinking Models Knowledge Base
node /path/to/skills/mckinsey-100y-knowledge-base/ima_api.cjs "openapi/wiki/v1/get_knowledge_list" '{"knowledge_base_id": "mbCVxK5Ig7E2CGRPdoDtJqnEYfYjIXJCQgtuPw1N0GQ=", "cursor": "", "limit": 50}' '{"clientId":"YOUR_CLIENT_ID","apiKey":"YOUR_API_KEY"}'
McKinsey 100-Year Knowledge Base Data Schema & Taxonomy
The McKinsey 100-Year Knowledge Base skill organizes its reference intelligence into four structured markdown files designed for progressive loading, supplemented by real-time queries.
| File / Module | Core Focus & Sub-topics | Key Assets Included |
|---|---|---|
references/01-core-methodology.md |
Core McKinsey consulting and structured thinking methods. | MECE, Pyramid Principle, 4S Method, Issue Tree, Blank Sheet thinking. |
references/02-work-skills.md |
Core professional execution and communications skills. | Professionalism, 7-step problem solving, structured writing, reading, and interrogation. |
references/03-industry-reports.md |
Global industry studies, reports, and macro trends. | GenAI economics, EV transitions, financial wealth management, retirement trends. |
references/04-thinking-models.md |
Highly structured thinking, system, and decision models. | 100+ thinking model visual maps, strategic frameworks, decision matrices. |
Metadata Taxonomy:
kb_id_100y:lEanMn3jC4JjNrNulwh5-uoANKemo_efKzqpqdZzG7k=(McKinsey 100-Year Knowledge Base ID)kb_id_models:mbCVxK5Ig7E2CGRPdoDtJqnEYfYjIXJCQgtuPw1N0GQ=(McKinsey Thinking Models Base ID)- Architecture style: progressive disclosure (lazy loads reference files based on prompt context to preserve token limits).
McKinsey 100-Year Knowledge Base Advanced Features
- Dual Knowledge Base Retrieval: Seamlessly orchestrates search queries between two separate databases—the 100-Year Knowledge Base and the Thinking Models Visual Library.
- Inter-Skill Orchestration: Natively cooperates with the
mckinsey-consultantskill, serving as the factual research layer while the consultant skill manages the interactive diagnostic workflow. - Context-Efficient Loading: Uses Progressive Disclosure to only ingest the exact module needed (
01-core-methodologyto04-thinking-models), avoiding excessive token consumption on LLMs. - Real-time IMA API Integration: Queries the latest curated whitepapers and frameworks directly from the live IMA wiki service instead of relying entirely on static offline data.
SKILL.md
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