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Brainstorming

Organization
frumu-ai
brainstorming

You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec.

Overview

Publisherfrumu-ai
Repositorytandem
Skill namebrainstorming
Stars
121
Forks
13
Bundled files
Instructions only
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 frumu-ai on GitHub. Read the source before you install it.

Installation

Install the 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.
  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/frumu-ai/tandem.git /tmp/tandem
mkdir -p .claude/skills
cp -r /tmp/tandem/apps/tandem-desktop/src-tauri/resources/skill-templates/brainstorming .claude/skills/brainstorming
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Brainstorming Ideas Into Plans

Purpose

Turn vague ideas into a validated plan or spec through natural, collaborative dialogue.

This is domain-generic: it can be used for product features, websites, backend systems, marketing campaigns, business ideas, content, workflows, or anything else.

Core rules:

  • Classify first: ask what type of brainstorming this is (multiple choice).
  • One question per message: keep cognitive load low.
  • Discovery-first: do not draft solutions until intent and constraints are understood.
  • Converge to a deliverable: end with a plan/spec that can be executed.

When to Use

  • Any new idea that could branch into multiple directions
  • Ambiguous requests (we should…, let’s build…, can we improve…?)
  • Planning work that needs clarified goals, scope, trade-offs, and acceptance criteria

Avoid When

  • The task is purely mechanical or trivial
  • The user provides a complete spec and explicitly wants immediate execution

Inputs That Improve Quality (Optional)

  • Constraints: budget, time, team size, deadline
  • Existing materials: docs, links, codebase, screenshots, examples
  • Success criteria: metrics, target outcomes, definition of done
  • Non-goals: what to avoid or explicitly exclude

Step 0: Classification (Required)

Start every brainstorming session by asking a single multiple-choice question:

What are we brainstorming today?

  • A) Product / feature (app, SaaS, tool)
  • B) Website / UX / UI / design
  • C) Engineering / architecture / systems (backend, infra, data)
  • D) Bugfix / troubleshooting / quality improvements
  • E) Content (blog, video, social, brand)
  • F) Marketing / growth / distribution
  • G) Business model / pricing / sales
  • H) Process / operations / team workflow
  • I) Something else (describe in one sentence)

Then ask one follow-up question that is specific to the chosen category.

Rule: until classification is answered, do not assume domain.

The Process

Phase 1: Discovery (One Question at a Time)

Ask one question per message. Prefer multiple choice when possible. Use this ladder in order; skip only if already answered.

1) The Why (Intent and Motivation)
  • What problem are we solving, and why now?
  • What happens if we do nothing?
2) The Who (Audience / User / Stakeholders)
  • Who is this for?
  • Who decides what good looks like?
3) The What (Scope)
  • What must be true for this to be considered successful?
  • What’s in-scope for MVP vs out-of-scope?

Use MoSCoW:

  • Must / Should / Could / Won’t (for now)
4) Constraints (Hard Limits)
  • Deadline, budget, team capacity
  • Tools/stack constraints
  • Compliance/legal/privacy constraints
  • Brand/tone constraints (for content/marketing)
5) Inputs and Dependencies
  • What do we need that we don’t control? (APIs, vendors, approvals, assets)
  • What existing systems/content must be reused?
6) Risks and Unknowns
  • What are the biggest uncertainties?
  • What’s the cost of being wrong?

Unknowns Register rule:

  • Any unknown becomes a discovery task with a timebox and decision point.
7) Definition of Done
  • What specifically will be delivered?
  • How will we validate it worked?

Phase 2: Synthesis (Reflect Back)

Summarize in 150–250 words:

  • Problem, audience, MVP scope, constraints, success criteria, open questions Then ask:
  • Is this accurate before I propose options?

Phase 3: Explore Approaches (2–3 Options)

Present 2–3 options with trade-offs and a recommendation:

  • Option A (Recommended): why it best fits constraints
  • Option B: simpler/faster alternative
  • Option C: more ambitious/extensible alternative

Each option includes:

  • Pros/cons
  • Key risks
  • Rough effort class (S/M/L) or time range if requested
  • What to validate first

Then ask:

  • Which option should we proceed with (A/B/C), or should I adjust?

Phase 4: Produce the Deliverable (Incremental Validation)

Once an option is chosen, produce the output in small sections (200–300 words) and ask after each:

  • Does this look right so far?

Choose the output type based on the classification:

If A) Product / feature
  • Goals and non-goals
  • User stories and acceptance criteria
  • UX flow (if relevant)
  • Data/logic overview
  • MVP vs V2
  • Rollout and metrics
If B) Website / UX / UI
  • Site goals and audience
  • Sitemap / IA
  • Page-level sections and messaging
  • Visual direction and components
  • SEO/accessibility basics
  • Analytics/events
If C) Engineering / architecture / systems
  • Requirements (functional and non-functional)
  • Components/services and responsibilities
  • Data flow and contracts
  • Failure modes and retries
  • Security and observability
  • Testing and rollout plan
If D) Bugfix / troubleshooting
  • Repro and root cause hypotheses
  • Fastest validation steps
  • Fix options and risk
  • Test plan
  • Rollback strategy
If E) Content
  • Audience and promise
  • Angle/hook options
  • Outline/script structure
  • Examples and CTAs
  • Distribution plan
If F) Marketing / growth
  • Target segment and channel assumptions
  • Positioning and messaging
  • Funnel (top to conversion)
  • Experiments and metrics
  • Timeline and budget assumptions
If G) Business model / pricing / sales
  • ICP and value props
  • Pricing hypotheses
  • Packaging options
  • Sales motion and objections
  • Metrics and tests
If H) Process / operations / workflow
  • Current state and pain points
  • Desired outcomes
  • Proposed process changes
  • Tools and responsibilities
  • Rollout and check-ins
If I) Something else
  • Ask what output format they want (plan, spec, checklist, outline, roadmap)

Output Handling

Always ask whether the user wants the final deliverable saved to a file. If yes, write a Markdown file and confirm the filename. Default filename: brainstorming-plan.md

Category-Specific Starter Questions (Pick One)

Product / feature

  • What user action should this enable that’s currently impossible or painful?

Website / UX / UI

  • What is the single most important action you want visitors to take?

Engineering / systems

  • What are the top 3 non-functional requirements: latency, cost, reliability, security, or something else?

Bugfix / troubleshooting

  • Do we have consistent reproduction steps (Yes/No/Not sure)?

Content

  • What platform is this for (YouTube, X, blog, email, TikTok, other)?

Marketing / growth

  • Which channel are we prioritizing first (SEO, paid ads, social, partnerships, outbound, other)?

Business model / pricing

  • Who is the buyer (end user, team lead, company)?

Process / workflow

  • What’s the current workflow step that causes the most friction?

Key Principles

  • Classify first (no domain assumptions)
  • One question at a time (always)
  • Multiple choice preferred (reduce friction)
  • Start broad, then narrow
  • Explore alternatives (2–3 options)
  • YAGNI ruthlessly (MVP-first)
  • Incremental validation (section-by-section)
  • Make assumptions explicit (and confirm them)
  • Stop and clarify whenever uncertainty appears

Frequently asked questions

What does the Brainstorming AI skill do?

You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec.

Why use Brainstorming on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/frumu-ai/tandem/tree/main/apps/tandem-desktop/src-tauri/resources/skill-templates/brainstorming. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Brainstorming?

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

Is the Brainstorming AI skill free?

It is published on GitHub by frumu-ai. 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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