Hypothesis Driven Data Analysis

Community

MichaelRochonnn

hypothesis-driven-data-analysis

Use this skill when analyzing data to find actionable business solutions. It helps you decide between hypothesis-driven thinking and data mining approaches, ensuring you prioritize logical causation over mere statistical correlation. Apply during strategic planning, product development, or marketing initiatives when you have access to data and a business problem to solve.

Overview

Publisher	MichaelRochonnn
Repository	`mckinsey-bcg-consulting`
Skill name	`hypothesis-driven-data-analysis`
Stars	77
Forks	16
Bundled files	1
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by MichaelRochonnn on GitHub. Read the source before you install it.

Installation

Install the Hypothesis Driven Data Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/hypothesis-driven-data-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/hypothesis-driven-data-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/MichaelRochonnn/mckinsey-bcg-consulting.git /tmp/mckinsey-bcg-consulting
mkdir -p .claude/skills
cp -r /tmp/mckinsey-bcg-consulting/hypothesis-driven-data-analysis .claude/skills/hypothesis-driven-data-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hypothesis Driven Data Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hypothesis Driven Data Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hypothesis Driven Data Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hypothesis-Driven Data Analysis

When to Use

You are analyzing data to find actionable business solutions
You have access to data (Big Data or Small Data) and a business problem to solve
You need to determine whether to trust correlations or develop hypotheses
You are working on strategic planning, product development, or marketing initiatives

Prerequisites

Access to relevant data sources
A clearly defined business problem
Understanding of the target customer or market context

Core Workflow

Step 1: Evaluate Data Mining Approach

When using Big Data or AI-driven analysis:

Identify correlations in the data (e.g., products purchased together, behavioral patterns)
Assess the risk - correlations may be coincidental or lack logical causality
Verify the logical "why" before implementing any actions based on the correlation

Warning: Do not rely solely on AI/Big Data outputs without logical verification.

Step 2: Apply Hypothesis Thinking

When developing a hypothesis-driven approach:

Imagine specific customer scenarios - create concrete customer personas with pain points and desired gains
Reduce complex problems to simple, meaningful hypotheses
Test the hypothesis even if it contradicts standard industry practices

Step 3: Decision Logic

Use this decision tree to determine your approach:

IF Big Data shows a correlation BUT no logical hypothesis exists → Be cautious of implementation
IF a strong, simple hypothesis exists based on customer empathy → Prioritize this over complex data analysis
IF both correlation and hypothesis align → Proceed with confidence

Key Constraints

Correlation does not equal causation - always seek the underlying logical explanation
Avoid basket analysis without logic - don't act on statistical patterns alone
Customer empathy is essential - hypotheses must be grounded in real customer needs

Output

An actionable business strategy derived from logical causation rather than just statistical correlation.

Anti-Patterns to Avoid

Implementing changes based solely on statistical correlations without understanding the "why"
Over-relying on AI/Big Data outputs without human verification
Ignoring simple, customer-centered hypotheses in favor of complex data analysis

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/examples-and-variables.md2 KB

Frequently asked questions

What does the Hypothesis Driven Data Analysis AI skill do?

Why use Hypothesis Driven Data Analysis on TypingMind?

Because you install it once and use it with any model. Hypothesis Driven Data Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hypothesis Driven Data Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/hypothesis-driven-data-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hypothesis Driven Data Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hypothesis Driven Data Analysis?

As many as you like. As long as a model supports skills, you can use Hypothesis Driven Data Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hypothesis Driven Data Analysis AI skill free?

It is published on GitHub by MichaelRochonnn. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Hypothesis-Driven Data Analysis

When to Use

Prerequisites

Core Workflow

Step 1: Evaluate Data Mining Approach

Step 2: Apply Hypothesis Thinking

Step 3: Decision Logic

Key Constraints

Output

Anti-Patterns to Avoid

Bundled files

Frequently asked questions

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