Six Hats logo

Six Hats

Community
danium
six-hats

Parallel thinking with six enforced perspectives, based on Edward de Bono's Six Thinking Hats® method - examine one decision through sequential passes for facts, feelings, risks, benefits, alternatives, and synthesis, never blending them. Use when a decision is being made too fast or from one angle, when everyone agrees suspiciously quickly, when the discussion mixes emotions with data, or for "six hats", "thinking hats", "look at this from every angle", "structured devil's advocate". Do NOT use for analytical work like debugging, code review, or implementation tasks.

Overview

Publisherdanium
Repositorylateral-thinking
Skill namesix-hats
Stars
325
Forks
24
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Six Hats 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/danium/lateral-thinking/tree/main/skills/six-hats
  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/danium/lateral-thinking.git /tmp/lateral-thinking
mkdir -p .claude/skills
cp -r /tmp/lateral-thinking/skills/six-hats .claude/skills/six-hats
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Six Hats 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 Six Hats 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 Six Hats 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.

Six Thinking Hats

What this technique does

Take one decision and run it through six deliberately separated perspectives, one at a time. Each pass wears a single "hat": facts, feelings, risks, benefits, alternatives, then synthesis. The power is in the separation — a group that argues all six dimensions at once collapses into whoever talks loudest, but a group that examines facts only, then feelings only, surfaces things the argument would have buried. It converts a tug-of-war into six clean passes over the same ground.

Based on Edward de Bono's Six Thinking Hats® method (Six Thinking Hats, 1985). Six Thinking Hats is a registered trademark of the de Bono Group; this skill is an independent educational implementation.

Workflow

Step 1: Confirm the target

A valid target is a decision — something with an option to accept, reject, or modify. "Should we ship X in v1?", "Do we adopt this dependency?", "Which of these two rollout plans?" all qualify. If the request is open-ended ideation with no decision to weigh ("what could we build next?"), this is the wrong technique — point the user to a generative technique such as ../random-stimulus/SKILL.md instead. If the target is unclear, ask one focused question: "What's the specific decision, and what are the options on the table?"

Refuse requests to perform analytical work — "debug this", "review this code", "implement this change" — and suggest an analytical approach instead. Redesigning or ideating about such a process is a valid target: "reinvent our code-review ritual" is in scope; "review this PR" is not.

Step 2: Run the six passes in fixed order

Run all six, each under its own clear heading, never blending. If material for one hat surfaces while wearing another, park it and raise it when its hat comes up.

  • White — facts only. State only what is verifiable. Name the missing data explicitly rather than guessing at it. No opinions, no interpretation.
  • Red — gut reactions. One line each, no justification permitted. The instant "this feels wrong" or "I'd be relieved" — recorded, not defended. Justifying a feeling turns it into a Black or Yellow point; don't.
  • Black — risks and failure modes. Each risk tied to a mechanism — how it actually goes wrong — not a vibe. "This could fail" is not a Black-hat point; "this fails if traffic doubles because the queue is unbounded" is.
  • Yellow — benefits and best-case. Each benefit tied to a mechanism too. Not "this would be great" but "this cuts onboarding time because the config step disappears."
  • Green — alternatives and modifications. Minimum three. At least one must abandon the premise of the decision entirely rather than tweak it.
  • Blue — synthesis. What did the hats disagree about? What piece of information would change the answer? End with a recommendation carrying a stated confidence level.

Step 3: Offer next moves

Offer, don't push: re-run a single hat deeper, take one of the Green alternatives through all six hats, switch to another technique, or stop here. Never pressure the user to commit to the recommendation.

Honesty mechanics

The Red hat must bite. It must contain at least one feeling that contradicts the emerging conclusion — the discomfort with a decision that otherwise looks clean, or the reluctance to reject something that fails on paper. If no such feeling genuinely surfaced, say so explicitly rather than manufacturing one: Red hat is suspiciously aligned — flagging possible motivated reasoning. A Red hat where every feeling agrees with the answer is a tell.

The Blue hat must not paper over the split. Name the strongest unresolved tension in plain terms — the one thing that, if it broke the other way, flips the recommendation. Synthesis is not the same as consensus; a real disagreement between the hats is more useful reported than smoothed.

What NOT to do

  • Don't blend the hats. A risk that occurs to you during Yellow gets deferred to Black, explicitly ("noting a risk here — holding it for Black"). Mixing dimensions is the exact failure the technique exists to prevent.
  • Don't let Blue introduce new material. Blue synthesizes what the other five hats produced. A fresh fact or risk appearing for the first time in Blue means an earlier hat was run lazily — go back.
  • Don't run this on a problem with no decision in it. No option to accept or reject means no target. Redirect to a generative technique.
  • Don't skip Red because "feelings aren't data." Surfacing the feelings is the hat's job. Unspoken reluctance sinks more decisions than any spreadsheet; the Red hat drags it into the open where it can be examined.

References

Bundled files

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

Frequently asked questions

What does the Six Hats AI skill do?

Parallel thinking with six enforced perspectives, based on Edward de Bono's Six Thinking Hats® method - examine one decision through sequential passes for facts, feelings, risks, benefits, alternatives, and synthesis, never blending them. Use when a decision is being made too fast or from one angle, when everyone agrees suspiciously quickly, when the discussion mixes emotions with data, or for "six hats", "thinking hats", "look at this from every angle", "structured devil's advocate". Do NOT use for analytical work like debugging, code review, or implementation tasks.

Why use Six Hats on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danium/lateral-thinking/tree/main/skills/six-hats. 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 Six Hats?

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 Six Hats?

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

Is the Six Hats AI skill free?

Yes. It is published on GitHub by danium 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇