How do I install the Consultant Mck skill?

Run npx skillmds@latest add nahisaho/consultant-mck in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Consultant Mck safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Consultant Mck?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Consultant Mck free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Consultant Mck?

nahisaho (@nahisaho) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Consultant Mck by nahisaho — Agent Skill

Name: Consultant Mck
Author: nahisaho

Consultant Mck

McKinsey style consulting skill for deep research and analysis. Features 7S Framework, Hypothesis-Driven Approach, Issue Tree, Pyramid Principle, Three Horizons, Granularity of Growth, OVA, Profit Pool Analysis, and SCP Analysis. McKinsey-style 5-phase workflow: Problem Definition → Hypothesis → Issue Decomposition → Data Collection/Analysis → Integration/Recommendation. Supports Deep Research MCP for enhanced web research capabilities.

nahisaho Updated Sep 17, 2026

File contents

Consultant MCK (McKinsey-Style Consulting)

Use This Skill When

A problem needs McKinsey-style hypothesis-driven decomposition and synthesis.
Strategy, growth, operating model, or market structure work must be framed into a structured consulting flow.
The final output must be decision-oriented, file-first, and presentation ready.

Local Resources

prompts/: phased flows for discovery, research, framework analysis, and reporting.
skills/: nested Agent Skills including orchestrator/SKILL.md for routing and framework application.

Required Inputs

Client objective, decision to support, and operating context.
Available evidence, hypotheses, assumptions, and delivery timeline.
Required output format, review gates, and success criteria.

Workflow

Confirm scope, evidence path, and the artifact set to save.
Route through the orchestrator or local helpers only when they materially improve the current task.
Save analyses, intermediate outputs, and recommendations to files instead of leaving results in chat.
Verify assumptions, traceability, and recommendation quality before finalizing conclusions.
Append skill selection, handoff I/O, and file writes to logs/process-log.jsonl when the execution harness requires trace logging.

Deliverables

report.md: problem, hypothesis, analysis, recommendation, and file inventory.
results/: issue trees, framework outputs, analyses, and evidence summaries.
figures/: English-only charts or synthesis visuals when helpful.
data/: processed or normalized source material when transformation occurs.

Quality Gates

The selected McKinsey-style framework matches the problem and evidence quality.
Hypotheses, assumptions, and calculations are traceable from inputs to conclusions.
Final outputs contain actionable recommendations and saved supporting artifacts.
report.md and, when used, logs/process-log.jsonl reference generated files.

---name: consultant-mckdescription: McKinsey style consulting skill for deep research and analysis. Features 7S Framework, Hypothesis-Driven Approach, Issue Tree, Pyramid Principle, Three Horizons, Granularity of Growth, OVA, Profit Pool Analysis, and SCP Analysis. McKinsey-style 5-phase workflow: Problem Definition → Hypothesis → Issue Decomposition → Data Collection/Analysis → Integration/Recommendation. Supports Deep Research MCP for enhanced web research capabilities.---# Consultant MCK (McKinsey-Style Consulting)McKinsey style consulting skill for deep research and analysis. Features 7S Framework, Hypothesis-Driven Approach, Issue Tree, Pyramid Principle, Three Horizons, Granularity of Growth, OVA, Profit Pool Analysis, and SCP Analysis. McKinsey-style 5-phase workflow: Problem Definition → Hypothesis → Issue Decomposition → Data Collection/Analysis → Integration/Recommendation. Supports Deep Research MCP for enhanced web research capabilities.## Use This Skill When- A problem needs McKinsey-style hypothesis-driven decomposition and synthesis.- Strategy, growth, operating model, or market structure work must be framed into a structured consulting flow.- The final output must be decision-oriented, file-first, and presentation ready.## Local Resources- `prompts/`: phased flows for discovery, research, framework analysis, and reporting.- `skills/`: nested Agent Skills including `orchestrator/SKILL.md` for routing and framework application.## Required Inputs- Client objective, decision to support, and operating context.- Available evidence, hypotheses, assumptions, and delivery timeline.- Required output format, review gates, and success criteria.## Workflow1. Confirm scope, evidence path, and the artifact set to save.2. Route through the orchestrator or local helpers only when they materially improve the current task.3. Save analyses, intermediate outputs, and recommendations to files instead of leaving results in chat.4. Verify assumptions, traceability, and recommendation quality before finalizing conclusions.5. Append skill selection, handoff I/O, and file writes to `logs/process-log.jsonl` when the execution harness requires trace logging.## Deliverables- `report.md`: problem, hypothesis, analysis, recommendation, and file inventory.- `results/`: issue trees, framework outputs, analyses, and evidence summaries.- `figures/`: English-only charts or synthesis visuals when helpful.- `data/`: processed or normalized source material when transformation occurs.## Quality Gates- The selected McKinsey-style framework matches the problem and evidence quality.- Hypotheses, assumptions, and calculations are traceable from inputs to conclusions.- Final outputs contain actionable recommendations and saved supporting artifacts.- `report.md` and, when used, `logs/process-log.jsonl` reference generated files.

Frequently asked questions