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Nw Brainstorming

Organization
nWave-ai
nw-brainstorming

Structured divergent thinking techniques — HMW framing, SCAMPER, Crazy 8s mechanics, and option diversity guarantees. Enforces strict separation of generation and evaluation phases.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-brainstorming
Stars
616
Forks
65
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 nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Brainstorming 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/nWave-ai/nWave/tree/main/nWave/skills/nw-brainstorming
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-brainstorming .claude/skills/nw-brainstorming
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Brainstorming 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 Nw Brainstorming 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 Nw Brainstorming 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.

Structured Brainstorming

The Separation Principle — Foundational Rule

Generation and evaluation CANNOT happen simultaneously. Osborn (1953): "You cannot get hot and cold water from the same faucet at the same time — you only get tepid water."

Consequence for the agent: All options must be generated before any option is scored. Never filter or rank during generation. Self-censorship during generation degrades both the quality of ideas and the quality of evaluation.


Phase 1: HMW Framing — Set Up the Ideation Space

Before generating any options, reframe the problem as a "How Might We" question.

Rules for valid HMW questions:

  • No embedded solutions: "How might we make onboarding faster?" ✓ vs "How might we add tooltips?" ✗
  • Outcome-oriented, not feature-oriented
  • Broad enough for genuinely different approaches
  • Positive framing (not "How might we avoid X?")

Example transformation:

  • Feature request: "Add a dashboard to show workflow status"
  • Bad HMW: "How might we build a better dashboard?"
  • Good HMW: "How might we give teams confidence that their workflow is progressing correctly?"

The good HMW opens the solution space: the answer isn't necessarily a dashboard.


Phase 2: SCAMPER — 7 Structurally Different Lenses

Apply each SCAMPER lens to the validated job to generate one option per letter. This guarantees structural diversity — each letter produces a categorically different type of option.

LetterLensQuestionWhat it produces
SSubstituteWhat if the core mechanism were replaced entirely?Alternative technology/approach
CCombineWhat if this job were merged with an adjacent job?Integrated solution
AAdaptWhat works well in a different domain? Could we borrow it?Cross-domain transfer
MModify/MagnifyWhat if the most important dimension were amplified?Focused excellence
PPut to other useWho else has this job? Could the solution serve them too?Market extension
EEliminateWhat if we removed the most complex part?Radical simplification
RReverseWhat if the workflow ran backwards? User and system switched roles?Inversion

Required output: At minimum, one option per SCAMPER letter. Name each option clearly. Do not evaluate during generation.


Phase 3: Crazy 8s Supplement — Volume and Self-Censorship Removal

After SCAMPER, generate 2-4 additional options by imagining you have only 1 minute per idea. The time pressure mechanic prevents evaluation creep.

Constraint: Each additional option must differ structurally from all SCAMPER options. Not a variation — a different type of approach.


Phase 4: Option Curation — Converge to 6

Before handing off to taste evaluation:

  1. List all generated options (7+ from SCAMPER + supplements)
  2. Remove exact duplicates only — not similar ones, exact
  3. Group options that are genuine variations of the same approach → keep the strongest representative
  4. Target: 6 options for evaluation (evidence-backed sweet spot: enough diversity, evaluable without overload)

Diversity test — each of the 6 options should answer yes to:

  • Different mechanism? (not just variation in degree)
  • Different assumption about user behavior?
  • Different cost/effort profile?

If two options share all three, they are variations — merge them.


Option Format

Each option in options-raw.md must follow:

### Option N: [Name]

**Core idea**: One sentence — what would a user actually experience?
**Key mechanism**: What makes this work?
**Key assumption**: What must be true for this to succeed?
**SCAMPER origin**: Which lens generated this? (or "Crazy 8s supplement")
**Closest competitor**: What existing product does this most?

Anti-Patterns to Avoid

Anti-patternWhy it failsCorrection
Generating one good idea and elaborating itConfirmation bias — narrows too earlyApply all SCAMPER letters before stopping
"Improved version of the existing approach"Not divergent — variation, not optionApply E (Eliminate) or R (Reverse)
Filtering during generation ("that won't work")Destroys divergent thinkingGenerate everything, filter in taste phase
Options that share the same core mechanismFalse diversityApply diversity test before curating
Skipping HMW framingSolution space collapses prematurelyHMW must precede any generation

DIVERGE Output for Brainstorming Phase

Produce docs/feature/{feature-id}/diverge/options-raw.md with:

  1. HMW question — the validated framing
  2. SCAMPER options — one per letter, named and described
  3. Crazy 8s supplements — 2-4 additional structurally distinct options
  4. Curated 6 — with diversity test results
  5. Eliminated options — brief note on why merged/removed

Gate: 6 curated options, each passing the 3-point diversity test, before taste evaluation begins.

Frequently asked questions

What does the Nw Brainstorming AI skill do?

Structured divergent thinking techniques — HMW framing, SCAMPER, Crazy 8s mechanics, and option diversity guarantees. Enforces strict separation of generation and evaluation phases.

Why use Nw Brainstorming on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-brainstorming. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Brainstorming?

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 Nw Brainstorming?

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

Is the Nw Brainstorming AI skill free?

Yes. It is published on GitHub by nWave-ai 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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