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Why

OrganizationPopular
NeoLabHQ
why

Iterative Five Whys root cause analysis drilling from symptoms to fundamentals

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill namewhy
Stars
1.8K
Forks
160
Bundled files
Instructions only
LicenseGPL-3.0
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/why
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/why .claude/skills/why
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Why 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 Why 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 Why 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.

Five Whys Analysis

Apply Five Whys root cause analysis to investigate issues by iteratively asking "why" to drill from symptoms to root causes.

Description

Iteratively ask "why" to move from surface symptoms to fundamental causes. Identifies systemic issues rather than quick fixes.

Usage

/why [issue_description]

Variables

  • ISSUE: Problem or symptom to analyze (default: prompt for input)
  • DEPTH: Number of "why" iterations (default: 5, adjust as needed)

Steps

  1. State the problem clearly
  2. Ask "Why did this happen?" and document the answer
  3. For that answer, ask "Why?" again
  4. Continue until reaching root cause (usually 5 iterations)
  5. Validate by working backwards: root cause → symptom
  6. Explore branches if multiple causes emerge
  7. Propose solutions addressing root causes, not symptoms

Examples

Example 1: Production Bug

Problem: Users see 500 error on checkout
Why 1: Payment service throws exception
Why 2: Request timeout after 30 seconds
Why 3: Database query takes 45 seconds
Why 4: Missing index on transactions table
Why 5: Index creation wasn't in migration scripts
Root Cause: Migration review process doesn't check query performance

Solution: Add query performance checks to migration PR template

Example 2: CI/CD Pipeline Failures

Problem: E2E tests fail intermittently
Why 1: Race condition in async test setup
Why 2: Test doesn't wait for database seed completion
Why 3: Seed function doesn't return promise
Why 4: TypeScript didn't catch missing return type
Why 5: strict mode not enabled in test config
Root Cause: Inconsistent TypeScript config between src and tests

Solution: Unify TypeScript config, enable strict mode everywhere

Example 3: Multi-Branch Analysis

Problem: Feature deployment takes 2 hours

Branch A (Build):
Why 1: Docker build takes 90 minutes
Why 2: No layer caching
Why 3: Dependencies reinstalled every time
Why 4: Cache invalidated by timestamp in Dockerfile
Root Cause A: Dockerfile uses current timestamp for versioning

Branch B (Tests):
Why 1: Test suite takes 30 minutes
Why 2: Integration tests run sequentially
Why 3: Test runner config has maxWorkers: 1
Why 4: Previous developer disabled parallelism due to flaky tests
Root Cause B: Flaky tests masked by disabling parallelism

Solutions: 
A) Remove timestamp from Dockerfile, use git SHA
B) Fix flaky tests, re-enable parallel test execution

Notes

  • Don't stop at symptoms; keep digging for systemic issues
  • Multiple root causes may exist - explore different branches
  • Document each "why" for future reference
  • Consider both technical and process-related causes
  • The magic isn't in exactly 5 whys - stop when you reach the true root cause
  • Stop when you hit systemic/process issues, not just technical details
  • Multiple root causes are common—explore branches separately
  • If "human error" appears, keep digging: why was error possible?
  • Document every "why" for future reference
  • Root cause usually involves: missing validation, missing docs, unclear process, or missing automation
  • Test solutions: implement → verify symptom resolved → monitor for recurrence

Frequently asked questions

What does the Why AI skill do?

Iterative Five Whys root cause analysis drilling from symptoms to fundamentals

Why use Why on TypingMind?

Because you install it once and use it with any model. Why 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 Why in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/why. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Why?

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 Why?

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

Is the Why AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 license. 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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