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MECE (Mutually Exclusive, Collectively Exhaustive)

Activate when: user says 'decompose this', 'structure this thinking', 'MECE', 'issue tree', 'Minto Pyramid', 'how do we cover all the cases without double-co... Skill: MECE (Mutually Exclusive, Collectively Exhaustive) Owner: deciqai Summary: Activate when: user says 'decompose this', 'structure this thinking', 'MECE', 'issue tree', 'Minto Pyramid', 'how do we cover all the cases without double-co... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:06:02.579Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/mece.json) v1.0.4 | 202

OpenClaw

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Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:mece

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-mece/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

120,868 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: mece
description: "Activate when: user says 'decompose this', 'structure this thinking', 'MECE', 'issue tree', 'Minto Pyramid', 'how do we cover all the cases without double-counting', a problem feels too big to think about cleanly, a list of options is messy or overlapping, an analysis keeps going in circles, or a presentation needs to survive hard scrutiny.
  Do NOT activate when: the problem is trivially small (lunch decisions), or the user is in purely creative/generative mode where premature structure would constrain exploration. More: deciqai.com/c/mece"
---

# MECE (Mutually Exclusive, Collectively Exhaustive)

## Overview

**MECE** is a decomposition principle: break a problem, set of options, or population into sub-groups that are **Mutually Exclusive** (no overlap) and **Collectively Exhaustive** (no gaps) — every relevant item covered exactly once. Operationalized by **Barbara Minto** at McKinsey (1963–1973) in *The Pyramid Principle* (1973; 3rd ed. 2002). The test: sum the pieces back to the whole; if they don't sum cleanly, the decomposition is broken.

**Compose:** use first-principles to reach root variables; pareto-principle to find load-bearing branches; critical-thinking to test whether categories are the *right* ones; occams-razor when multiple MECE structures fit — pick the simplest.

## When to Use

- Problem feels **too big to think about cleanly** — surface area is unclear
- **List of options** is messy or overlapping — competing answers that aren't parallel
- Analysis is **going in circles** — same issues reappear because they're not separated
- **Presentation** must convince hard-to-convince listeners
- Team converging on a hypothesis **without considering the full space** of alternatives
- Structuring an **AI strategy / AI-stack / AI-adoption** discussion so the layers (chips / cloud / models / apps) or use cases have no overlaps and no gaps — cutting through AI hype to a complete, non-redundant map
- Someone says: *"MECE," "decompose this," "issue tree," "structure this thinking"*

**When NOT to use:** trivially small problem; purely creative/generative mode; genuinely non-decomposable question (some ethical/aesthetic problems resist this); decomposition already known and well-trodden.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete case → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line what-it-is: MECE is **breaking a problem into pieces that don't overlap AND don't leave gaps** — every relevant thing is covered exactly once. The check: do the pieces sum back to the whole?
2. Check fit against When to Use / When NOT to use. Trivial problem → redirect. Creative exploration → not yet.
3. Elicit their specific problem to decompose. *"My business has problems"* is too vague; need a concrete

_meta.json

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  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "mece",
  "version": "1.0.5",
  "publishedAt": 1784225162579
}

references/sources.md

# Sources — mece

> *Primary sources for the [mece](../SKILL.md) skill.*

- **Minto, Barbara.** *The Pyramid Principle: Logic in Writing, Thinking and Problem Solving*. 1st ed. 1978; 3rd ed., Financial Times Prentice Hall (Pearson), 2002. **Primary source for MECE and pyramid-structured analysis**.
- **Rasiel, Ethan M.** *The McKinsey Way: Using the Techniques of the World's Top Strategic Consultants to Help You and Your Business*. McGraw-Hill, 1999. **Documentation of McKinsey's internal use of MECE**.
- **Gerstner, Louis V., Jr.** *Who Says Elephants Can't Dance? Inside IBM's Historic Turnaround*. HarperBusiness, 2002. **Primary source for the IBM 1993 turnaround decision case**.
- **IBM Corporation,** Annual Reports and SEC 10-K filings, 1993–2002. **Primary-source empirical outcome data**: https://www.ibm.com/investor/
- The popular phrase "no overlap, no gap" is the **operational summary** — accurate, but the rigor is in the *test* (sum back to the whole), not the slogan.
- **Nvidia Corporation,** quarterly earnings releases and investor materials, FY2024–FY2025: https://investor.nvidia.com/ — **primary-source data on AI data-center GPU demand and the compute layer of the AI stack** (used in the 2024–2026 AI-strategy example).
- **Microsoft, Amazon (AWS), and Alphabet (Google),** quarterly earnings and capital-expenditure guidance, 2024–2025 (each firm's investor-relations disclosures) — **primary-source documentation of hyperscaler AI-infrastructure build-out**; the chips / cloud / models / applications layering is the widely-used industry taxonomy for the AI value chain as of early 2026.

examples/ai-stack-market-strategy-decomposition-2024-2026.md

# Method in Action: Structuring an AI-Stack Market Strategy MECE (2024–2026)

> *Example for the [mece](../SKILL.md) skill.*

A worked example for the AI-hype era. When a leadership team says *"we need an AI strategy,"* the conversation usually collapses into a jumble of overlapping buzzwords — foundation models, GPUs, copilots, agents, RAG — that double-count some things and silently omit others. MECE turns that jumble into a structure the strategy discussion can actually close over.

The observable backdrop (all public and widely reported before early 2026): the generative-AI wave that began with the November 2022 release of OpenAI's ChatGPT accelerated through 2024–2025. Nvidia's data-center GPUs became the scarce input for training frontier models; the major cloud providers (Microsoft Azure, Amazon AWS, Google Cloud) raised capital-expenditure guidance sharply to build AI infrastructure; a small set of labs (OpenAI, Anthropic, Google DeepMind, Meta, Mistral, and others) shipped competing frontier and open-weight models; and an application layer of copilots and "agents" proliferated on top. Alongside the build-out ran a persistent hype-vs-adoption debate — how much of the spending would convert into durable enterprise value.

The strategy question: *where in this stack should we play, and is our current plan covering the whole board or just the loudest layer?* Walk the **MECE Decomposition**.

- **State the whole precisely (Step 1):** Not "our AI strategy." The whole is *"the full set of value-capture positions in the generative-AI market that our company could occupy or depend on, 2024–2026."* Measurable subject: every dollar of AI spend flows through some layer of the stack — we want a partition of those layers.

- **Propose a top-level split (Step 2):** Segment by **layer of the AI stack** — the dimension that matches how value and margin actually flow:
1. **Compute / chips** — AI accelerators and the hardware supply chain (Nvidia GPUs, custom silicon like Google TPUs and AWS Trainium, memory, networking).
2. **Cloud / infrastructure** — the hosting, orchestration, and MLOps layer that rents that compute (hyperscaler AI platforms, GPU clouds, inference serving).
3. **Models / foundation models** — the trained frontier and open-weight models and the labs that produce them.
4. **Applications** — the software that end-users touch: copilots, vertical AI apps, and agentic products built on the models.

- **Test Mutual Exclusivity (Step 3):** Check every pair. A GPU (layer 1) is a physical accelerator; a cloud region renting it out (layer 2) is a service contract; the model weights (layer 3) are trained artifacts; the app (layer 4) is the end-user product. A given *dollar of activity* sits in exactly one layer. The genuine trap is vertical integration — Google designs chips **and** runs a cloud **and** trains models **and** ships apps; Microsoft partners on models **and** sells cloud **and** ships Copilot. But MECE partitions the *activit

examples/lou-gerstner-ibm-turnaround-decomposition-1993.md

# Method in Action: Lou Gerstner's IBM Turnaround Decomposition (1993)

> *Example for the [mece](../SKILL.md) skill.*

A worked example. Not management folklore — primary-source documented in Lou Gerstner's own account *Who Says Elephants Can't Dance?* (HarperBusiness, 2002).

In **April 1993**, **Louis V. Gerstner Jr.** took over as CEO of **IBM**, then in the worst crisis in its history. IBM had lost $16 billion over the prior three years, stock was at a 17-year low, and the board's then-consensus plan was to **break up the company** into autonomous business units — selling some, spinning others off. The breakup plan had been formally proposed by an internal task force, supported by external consultants (including parts of McKinsey), and was the public expectation when Gerstner arrived.

Gerstner, in his first 90 days, made what he later called "the most important decision of my career": he stopped to ask whether the breakup decomposition was *MECE*. The board's analysis had decomposed IBM by **product line** (mainframes, PCs, software, services, etc.), concluding that each line could function as a standalone business. Gerstner asked a different question, with a different decomposition:

> "Sometimes the most important consulting work is teaching people to ask different questions. The fundamental issue was not 'should we break the company up?' but 'what does the customer want?'. When I asked our biggest customers, none of them wanted to deal with five smaller IBMs. They wanted exactly the opposite — they wanted an *integrated solutions provider* who could handle the whole stack."
> — Lou Gerstner, *Who Says Elephants Can't Dance?* (HarperBusiness, 2002), ch. 8.

Walk the MECE Decomposition on the IBM 1993 decision:

- **The whole (Step 1):** *"How should IBM be structured to maximize long-term shareholder value, given the 1993 market position?"*
- **The board's decomposition (alternative considered):** By product line — Mainframes / PCs / Software / Services / Storage. **MECE on product**, but missing the question of how customers actually buy.
- **Gerstner's proposed top-level split (Step 2):** **By how customers buy** — Customers who want point products (single hardware or software item) / Customers who want integrated multi-vendor solutions / Customers who want a single full-stack provider managed under one contract.
- **ME test (Step 3):** Each customer at a given time buys in *one* of these three modes. Mutually exclusive ✓.
- **CE test (Step 4):** Are there other modes? Customers who want pure consulting without products? Yes — add a fourth branch "Pure advisory." Sum of the four branches covers all enterprise IT customers in 1993. Exhaustive ✓.
- **Load-bearing branch (Step 6):** Mode 3 (single full-stack provider) was the segment that **only IBM could serve** — competitors (Microsoft, Sun, Oracle) were strong in single product categories but had no integrated full-stack offering. **This segment was growing and most profitable.**
- **Ac

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