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

v0.2.0

关于此技能

适用场景: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. 本地化技能摘要: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

功能特性

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

# 核心主题

Light-Brands Light-Brands
[0]
[0]
更新于: 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

适用场景: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. 本地化技能摘要: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

核心价值

推荐说明: 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. This AI

适用 Agent 类型

适用场景: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate.

赋予的主要能力 · brainstorming

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

! 使用限制与门槛

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

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 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

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

brainstorming 是什么?

适用场景: Ideal for AI agents that need core principle: ask questions to understand, present options to explore, validate. 本地化技能摘要: <objective Turn rough ideas into fully-formed designs through natural collaborative dialogue. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

如何安装 brainstorming?

运行命令:npx killer-skills add Light-Brands/planetary-party/brainstorming。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

brainstorming 适用于哪些场景?

典型场景包括:适用任务: Applying Core principle: Ask questions to understand, present options to explore, validate、适用任务: Applying sections to refine. </objective、适用任务: Applying Skip for clear mechanical tasks with obvious solutions, well-defined requirements with。

brainstorming 支持哪些 IDE 或 Agent?

该技能兼容 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。可使用 Killer-Skills CLI 一条命令通用安装。

brainstorming 有哪些限制?

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

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add Light-Brands/planetary-party/brainstorming。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    brainstorming 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

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>

相关技能

寻找 brainstorming 的替代方案 (Alternative) 或可搭配使用的同类 community Skill?探索以下相关开源技能。

查看全部

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

333.8k
0
AI

widget-generator

Logo of f
f

为prompts.chat的信息反馈系统生成可定制的插件小部件

149.6k
0
AI

flags

Logo of vercel
vercel

React 框架

138.4k
0
浏览器

pr-review

Logo of pytorch
pytorch

Python中具有强大GPU加速的张量和动态神经网络

98.6k
0
开发者工具