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Brainstorming

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xenitV1
brainstorming

Design-first methodology. Explore user intent, requirements and design before implementation. Turn ideas into fully formed specs through collaborative dialogue.

Overview

PublisherxenitV1
Repositoryclaude-code-maestro
Skill namebrainstorming
Stars
231
Forks
34
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 xenitV1 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/xenitV1/claude-code-maestro.git /tmp/claude-code-maestro
mkdir -p .claude/skills
cp -r /tmp/claude-code-maestro/skills/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.

<domain_overview>

💡 BRAINSTORMING: DESIGN BEFORE CODE

Philosophy: Understanding comes before implementation. A well-designed solution is half-implemented. Never code without a clear design.

HALLUCINATION FIREWALL MANDATE (CRITICAL): Never propose software components or libraries without verification. AI-generated designs frequently fail by hallucinating non-existent packages or misinterpreting their capabilities. Every recommended 3rd-party library MUST be validated using npm info or equivalent before the plan is finalized. Furthermore, you MUST provide at least one 'Counter-Architecture' (Steel-man argument) that challenges your primary recommendation to prevent homogenized or biased designs. Help turn ideas into fully formed designs and specs through natural collaborative dialogue. Process:

  1. Understand the current project context
  2. Ask questions one at a time to refine the idea
  3. Present the design in small sections (200-300 words)
  4. Check after each section whether it looks right

📋 WHEN TO USE

MUST use before:

  • Creating new features
  • Building new components
  • Adding significant functionality
  • Modifying core behavior
  • Any task that takes more than 30 minutes Skip only for:
  • Simple bug fixes with obvious solutions
  • Documentation updates
  • Trivial configuration changes </domain_overview> <process_workflow>

🔄 THE PROCESS

Phase 1: Understanding the Idea

First, check current project state:

  • Review relevant files and docs
  • Check recent commits
  • Understand existing patterns Then ask questions one at a time:
  • MANDATORY: Use the AskUserQuestion tool for ALL questions.
  • Prefer multiple choice options within the tool whenever possible.
  • Open-ended questions should also use AskUserQuestion (users can use the 'Other' option).
  • Only one question per tool call.
  • If topic needs more exploration, break into multiple sequential tool calls. Focus on understanding:
  • Purpose: What problem does this solve?
  • Constraints: What limitations exist?
  • Success criteria: How do we know it works?
  • Edge cases: What could go wrong?

Phase 2: Exploring Approaches

Always propose 2-3 different approaches with trade-offs:

I see three possible approaches:
**Option A: [Name]**
- Pros: Simple, fast to implement
- Cons: May not scale, harder to test
- Best for: Quick prototypes
**Option B: [Name]**
- Pros: Scalable, well-tested pattern
- Cons: More complex, longer implementation
- Best for: Production systems
**Option C: [Name]**
- Pros: Flexible, future-proof
- Cons: Over-engineered for current needs
- Best for: When requirements are uncertain
**My recommendation:** Option B because [reasoning]
Which approach resonates with your goals?

Lead with your recommended option and explain why.

Phase 3: Presenting the Design

Once you understand what you're building, present the design:

  1. Break it into sections of 200-300 words
  2. Ask after each section: "Does this look right so far?"
  3. Be ready to go back and clarify if something doesn't make sense Cover these areas:
  • Architecture: How components fit together
  • Components: What pieces we need to build
  • Data flow: How information moves through the system
  • Error handling: What happens when things fail
  • Testing: How we verify it works

Phase 4: Documentation

After design is validated:

  1. Write the design to docs/plans/YYYY-MM-DD-<topic>-design.md
  2. Commit the design document to git
  3. Ask: "Ready to set up for implementation?" </process_workflow> <methodology_protocols>

🎤 QUESTION TECHNIQUES

Multiple Choice (MANDATORY TOOL USE)

Always use the AskUserQuestion tool for structured feedback:

json
{
  "questions": [
    {
      "header": "Auth Method",
      "question": "How should users authenticate?",
      "options": [
        {"label": "JWT Tokens", "description": "Stateless, scalable"},
        {"label": "Server Sessions", "description": "Simple, secure"},
        {"label": "OAuth Only", "description": "Delegate to providers"}
      ],
      "multiSelect": false
    }
  ]
}

Open-Ended (Using Tool)

Even for open-ended questions, use the tool. The CLI will provide an "Other" option for custom text input. "What's the most important user story for this feature?"

Clarifying

"You mentioned 'fast' - what response time would feel fast enough?"

🚫 ANTI-PATTERNS TO AVOID

Anti-PatternBetter Approach
Multiple questions at onceOne question per message
Jumping to implementationComplete design first
Assuming requirementsAsk to confirm
Presenting 1000-word designs200-300 word sections
Ignoring trade-offsAlways present alternatives
Skipping edge casesExplore failure modes
</methodology_protocols>
<design_artifacts>

📝 DESIGN DOCUMENT TEMPLATE

markdown
# [Feature Name] Design
**Date:** YYYY-MM-DD
**Author:** Grandmaster (with user collaboration)
**Status:** Draft | Approved | Implemented
## Problem Statement
What problem are we solving? Why does it matter?
## Goals
- Primary goal
- Secondary goals
- Non-goals (explicitly out of scope)
## Approach
### Architecture
How components fit together.
### Components
1. **Component A**
   - Purpose
   - Interface
   - Dependencies
2. **Component B**
   - Purpose
   - Interface
   - Dependencies
### Data Flow
1. User action triggers X
2. X calls Y with Z
3. Y returns result
4. Result displayed to user
### Error Handling
| Error | Handling | User Message |
|-------|----------|--------------|
| Network failure | Retry 3x | "Connection lost, retrying..." |
| Invalid input | Reject | "Please check your input" |
## Testing Strategy
- Unit tests for each component
- Integration test for happy path
- Edge case tests for error handling
## Open Questions
- [ ] Question 1
- [ ] Question 2
## Decision Log
| Date | Decision | Rationale |
|------|----------|-----------|
| YYYY-MM-DD | Chose Option B | Better scalability |

</design_artifacts> <integration_protocols>

🔗 INTEGRATION WITH MAESTRO

Triggering Brainstorming

User can invoke explicitly:

/maestro design [feature description]

Or system detects complex task and suggests:

This looks like a significant feature. Would you like to 
brainstorm the design first, or proceed directly?

After Brainstorming

  1. If continuing to implementation:
    • Use @planning-mastery to create detailed plan
    • Use @git-worktrees to create isolated workspace
  2. If pausing:
    • Design document is saved
    • Can resume later with /maestro plan [design-doc]

🔗 RALPH WIGGUM INTEGRATION

When Ralph Wiggum is active with "Feature Mode":

  1. Before first iteration: Run brainstorming phase
  2. Design document: Required before implementation begins
  3. Scope lock: Don't add features not in design
  4. Design changes: Require explicit approval </integration_protocols> <audit_and_reference>

📋 KEY PRINCIPLES

PrincipleDescription
One question at a timeDon't overwhelm with multiple questions
Multiple choice preferredEasier to answer than open-ended
YAGNI ruthlesslyRemove unnecessary features from designs
Explore alternativesAlways propose 2-3 approaches
Incremental validationPresent design in sections, validate each
Be flexibleGo back and clarify when needed

🔗 RELATED SKILLS

  • @planning-mastery - Create implementation plan from design
  • @git-worktrees - Set up isolated workspace
  • @tdd-mastery - Implement with tests first
  • @clean-code - Quality standards for implementation </audit_and_reference>

Frequently asked questions

What does the Brainstorming AI skill do?

Design-first methodology. Explore user intent, requirements and design before implementation. Turn ideas into fully formed specs through collaborative dialogue.

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/xenitV1/claude-code-maestro/tree/main/skills/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?

Yes. It is published on GitHub by xenitV1 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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