brainstorming — for Claude Code brainstorming, planetary-party, community, for Claude Code, ide skills, objective, fully-formed, designs, through, natural

v0.2.0

Acerca de este Skill

Escenario recomendado: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. Resumen localizado: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Características

Core principle: Ask questions to understand, present options to explore, validate
sections to refine. </objective
Skip for clear mechanical tasks with obvious solutions, well-defined requirements with
standard implementations, or simple bug fixes. </when-to-use
<understanding-context

# Core Topics

Light-Brands Light-Brands
[0]
[0]
Updated: 2/3/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
8/11
Quality Score
35
Canonical Locale
en
Detected Body Locale
en

Escenario recomendado: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. Resumen localizado: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

¿Por qué usar esta habilidad?

Recomendacion: brainstorming helps agents core principle: ask questions to understand, present options to explore, validate. <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue.

Mejor para

Escenario recomendado: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate.

Casos de uso accionables for brainstorming

Caso de uso: Applying Core principle: Ask questions to understand, present options to explore, validate
Caso de uso: Applying sections to refine. </objective
Caso de uso: Applying Skip for clear mechanical tasks with obvious solutions, well-defined requirements with

! Seguridad y limitaciones

  • Limitacion: One question per message. If a topic needs more exploration, break it into multiple
  • Limitacion: questions. Don't overwhelm with a list of questions. </understanding-context
  • Limitacion: Overkill unless we need independent scaling.

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is brainstorming?

Escenario recomendado: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. Resumen localizado: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install brainstorming?

Run the command: npx killer-skills add Light-Brands/planetary-party/brainstorming. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for brainstorming?

Key use cases include: Caso de uso: Applying Core principle: Ask questions to understand, present options to explore, validate, Caso de uso: Applying sections to refine. </objective, Caso de uso: Applying Skip for clear mechanical tasks with obvious solutions, well-defined requirements with.

Which IDEs are compatible with brainstorming?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for brainstorming?

Limitacion: One question per message. If a topic needs more exploration, break it into multiple. Limitacion: questions. Don't overwhelm with a list of questions. </understanding-context. Limitacion: Overkill unless we need independent scaling..

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add Light-Brands/planetary-party/brainstorming. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use brainstorming immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

brainstorming

<objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence
<objective> Turn rough ideas into fully-formed designs through natural collaborative dialogue. Understand the context, explore alternatives, validate incrementally.

Core principle: Ask questions to understand, present options to explore, validate sections to refine. </objective>

<when-to-use> Use brainstorming when you have a rough idea but unclear implementation, multiple approaches exist and you need to choose, requirements are fuzzy or incomplete, or design decisions need validation before coding.

Skip for clear mechanical tasks with obvious solutions, well-defined requirements with standard implementations, or simple bug fixes. </when-to-use>

<understanding-context> Explore the current project state. Check existing files, documentation, recent commits. Understand what's already built.

Ask questions one at a time to refine the idea. Use multiple choice when possible - easier to answer than open-ended. Focus on understanding purpose (what problem does this solve?), constraints (what limits the solution?), and success criteria (how do we know it works?).

One question per message. If a topic needs more exploration, break it into multiple questions. Don't overwhelm with a list of questions. </understanding-context>

<exploring-alternatives> Propose different approaches with their tradeoffs. Present conversationally, showing all options first before making a recommendation.

Example pattern: "I see three main approaches:

  1. Direct integration - Fast to implement but creates coupling. Good if this is temporary.

  2. Event-driven - More flexible, better separation, but adds complexity. Worth it if we'll extend this.

  3. Separate service - Maximum isolation, easier to scale, but operational overhead. Overkill unless we need independent scaling.

I'd recommend #2 (event-driven) because the requirements suggest we'll add features here, and the loose coupling will make that easier. What do you think?"

Present options before recommendation. LLMs process information sequentially - showing options first lets them fully consider each alternative before being influenced by a recommendation. The recommendation comes after all options have been presented.

Make a clear recommendation - pick one approach and explain why it fits best. Don't hedge or suggest combining approaches.

Avoid defaulting to hybrid approaches. Hybrid solutions are rarely the right answer. They often combine the complexity of multiple approaches without clear benefits. Only suggest a hybrid if there's a specific, compelling reason why a pure approach won't work.

Structure alternatives clearly - each option should be distinct with clear tradeoffs. If options are too similar, you haven't explored the design space enough.

Explain the choice criteria - make explicit what factors led to your recommendation (simplicity, performance, maintainability, etc.). This helps validate whether the recommendation aligns with priorities.

Let the human partner react after your recommendation. They may have constraints or priorities you didn't consider. </exploring-alternatives>

<presenting-design> Once you understand what you're building, present the design in small, manageable sections covering architecture and component structure, data flow and state management, error handling and edge cases, and testing approach.

Ask after each section whether it looks right. Be ready to go back and clarify if something doesn't make sense.

This incremental validation catches misunderstandings early before you've written a complete design document. </presenting-design>

<after-validation> Write the validated design to docs/plans/[topic]-design.md. Keep it concise and focused on decisions and rationale, not implementation details.

Commit the design document to git so it's tracked with the project.

If continuing to implementation, ask whether to proceed. Set up an isolated workspace for development (git worktree or feature branch). Create a detailed implementation plan breaking the design into concrete tasks. </after-validation>

<key-principles> One question at a time. Don't list multiple questions. Ask one, get an answer, ask the next.

Multiple choice preferred. "Should we use events or direct calls?" is easier than "How should components communicate?"

YAGNI ruthlessly. Remove unnecessary features from designs. Build what's needed, not what might be needed someday.

Explore alternatives always. Present multiple approaches before settling on one. This surfaces tradeoffs. Choose one clear recommendation - avoid defaulting to hybrid approaches which rarely solve the problem well.

Incremental validation. Present design in sections, validate each before continuing. Don't write a complete design then ask for feedback - you might be heading the wrong direction.

Be flexible. When something doesn't make sense to your partner, go back and clarify. Don't defend the design, refine it. </key-principles>

<common-pitfalls> Don't ask many questions at once. Don't present a complete design without incremental validation. Don't skip exploring alternatives. Don't add features beyond stated requirements. Don't continue with a design that confuses your partner - go back and clarify first. </common-pitfalls>

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