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Thinking Five Whys Plus

CommunityPopular
tjboudreaux
thinking-five-whys-plus

When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-five-whys-plus
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
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 tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Five Whys Plus 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.
  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/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-five-whys-plus .claude/skills/thinking-five-whys-plus
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Five Whys Plus 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 Thinking Five Whys Plus 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 Thinking Five Whys Plus 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 Plus

Build an evidence-linked causal chain from a known proximate fault to an actionable systemic root, then prescribe a countermeasure that would have blocked it.

When to Use

  • A fault is already localized to a component, path, or subsystem.
  • The proximate cause is known, but recurrence or systemic root is not.
  • Prior symptom-only fixes failed and the failure returned.
  • You need prevention that addresses why the proximate cause was possible.

When NOT to Use

  • Fault is not localized or multiple candidate causes remain — discriminate first.
  • Root is evidenced and actionable — fix it.
  • No evidence can answer the next "why" — gather evidence.
  • One-off error without a process, tooling, or design enabler.
  • Selective defect needing IS/IS-NOT analysis.
  • Forward failure forecasting before commitment.

Procedure

  1. State the localized problem. Record the observable symptom, confirmed component/path, time window, scope, and impact. Stop if localization is missing.
  2. Chain why with evidence. Make the prior answer the new effect; propose a cause; cite logs, code, config, metrics, or witnesses; name and rule out alternatives. Stop on speculation.
  3. Branch before descending. Ask what else could produce the same effect; keep every evidenced independent pathway. Treat the retained branches as a causal set rather than forcing one root.
  4. Counterfactually test each pathway and the set. For each candidate ask, "Would removing this cause block this pathway?" Then ask, "Would removing the retained set block the observed failure?" Retain independently sufficient branches whose removal blocks their own pathway; do not reject them merely because another branch could still cause the failure.
  5. Enforce stop criteria per branch. Stop a branch only when its cause is evidenced, controllable, actionable, recurrence-preventing, non-blame (not merely "someone erred"), and counterfactual-positive for that pathway. If it hits human error, ask why the error was possible.
  6. Prescribe a countermeasure for every retained root. Name the systemic fix, owner-capable action, and how recurrence will be checked. Prefer removing enabling conditions over only patching the proximate symptom.
  7. Stop when verified or blocked. Halt when stop criteria pass and a countermeasure is specified, or when further steps lack evidence. Do not pad by habit.

Output

text
Problem: <localized symptom + component>
Chain:
  Why1: <cause> | Evidence: <...> | Ruled out: <...>
  WhyN: ...
Causal set check: would removing all retained roots block the failure? yes/no
Roots:
  - <systemic cause> | Pathway counterfactual: <...>
    Countermeasure: <action that removes the enabling condition>
    Verification: <how to confirm this pathway is blocked>

Verification

  • Falsify the chain if any step lacks evidence, if alternatives were never considered, if a retained root does not pass its pathway counterfactual, or if the full causal set does not explain the failure.
  • Stop applying this skill once the root is fixed and verified, or when the problem is still pre-localization.
  • Over-application guard: skip a full chain for a single known one-line defect; do not end at blame; do not invent depth without data.

Frequently asked questions

What does the Thinking Five Whys Plus AI skill do?

When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.

Why use Thinking Five Whys Plus on TypingMind?

Because you install it once and use it with any model. Thinking Five Whys Plus 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 Thinking Five Whys Plus in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-five-whys-plus. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Five Whys Plus?

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 Thinking Five Whys Plus?

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

Is the Thinking Five Whys Plus AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT 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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