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brainstorm头脑风暴

Agent Skill

brainstorm 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,067

周安装

44

GitHub Stars

公开资料未说明

下载量

348
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:brainstorm(头脑风暴)
来源仓库:https://github.com/witooh/skills
仓库路径:skills/brainstorm
安装命令:
npx skills add https://github.com/witooh/skills --skill brainstorm
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/witooh/skills --skill brainstorm

简介

brainstorm 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装并使用。
  • 建议确认权限范围和维护状态,避免触发联网或文件读写。
  • brainstorm 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Brainstorm

Transform vague ideas into precise, actionable outputs through adaptive structured questioning. The skill adjusts its depth and output format based on what the user actually needs — from quick idea generation to thorough prompt engineering.

Quick Start

  1. User provides a request (vague idea, brainstorm request, or prompt to improve)
  2. Triage — Classify into one of three modes: Prompt, Explore, or Focused
  3. Run the appropriate discovery flow (3–7 questions depending on mode)
  4. Produce the right output type for the mode
  5. Offer next steps

Tools

ToolPurpose
AskUserQuestionAsk the user ONE question at a time. Claude Code: native AskUserQuestion with options (see AskUserQuestion Usage below). Copilot: ask_user with choices. Kiro/other: plain text with numbered options.
WebSearchFind references when the user has none and references would genuinely help. Claude Code: WebSearch. Copilot/Kiro: web_search.
AgentDelegate to Plan subagent. Claude Code: Agent(subagent_type: "Plan"). Copilot: task(agent_type: "general-purpose") + # Role: Planner block. Kiro: use_subagent. Fallback: create plan inline.

AskUserQuestion Usage

Use one question per call even though the API supports up to 4 — multiple questions get shallow answers.

Each question requires:

  • question: Clear question text ending with ?
  • header: Short label (max 12 chars) displayed as a chip, e.g. "Goal", "Priority", "Approach"
  • options: Array of 2-4 choices — each with label (1-5 words) and description (explains trade-offs). "Other" is added automatically — do not include one.
  • multiSelect: Set to true only when choices are not mutually exclusive. Omit otherwise (defaults to false).

Put your recommended option first and append "(Recommended)" to its label.

Example: {"questions": [{"question": "Which matters more?", "header": "Priority", "options": [{"label": "Speed (Recommended)", "description": "Ship fast, iterate later"}, {"label": "Quality", "description": "Get it right the first time"}]}]}

Core Principles

  1. Don't answer before you understand. The urge to help immediately produces generic output. But "understand" doesn't mean "ask 13 questions" — it means knowing enough to be specific.
  2. One question at a time via tool. Multiple questions get shallow answers. Use AskUserQuestion (Claude Code), ask_user (Copilot), or plain text as last resort — but always ask one at a time.
  3. Prefer multiple choice. Provide options when the answer space is predictable. Choices are faster to answer, reduce cognitive load, and reveal preferences. Use open-ended only when the answer truly can't be predicted.
  4. Mirror the user's language. Don't introduce jargon they didn't use.
  5. Ask about life, not the domain. Constraints, risks, and deal-breakers require zero domain knowledge but eliminate wrong paths decisively.
  6. Never re-ask what's already known. Track information from the initial prompt and all answers.
  7. Respect the user's time. Match question depth to request complexity. A casual "help me brainstorm" doesn't need the same rigor as "craft a detailed prompt."

Triage — Choosing the Right Mode

Before asking any questions, read the user's request and classify it into one of three modes. This happens internally — don't ask the user which mode they want.

Prompt Mode

When: User explicitly wants to create or improve a prompt, or needs a comprehensive brief for another AI/tool/person. Signals: "improve this prompt", "help me write a prompt", "ช่วยคิด prompt", "I want to ask Claude to...", mentions using the output with another AI. Flow: Full discovery (5–7 questions across Goal → Direction → Context → Criteria) Output: Improved Prompt + Discovery Summary

Explore Mode

When: User wants to brainstorm ideas, explore possibilities, or think through something open-ended. Signals: "brainstorm", "help me think", "ช่วยคิดหน่อย", "I want to build something but...", "what should I...", "any ideas for..." Flow: Light discovery (3–5 questions) — understand goals + constraints quickly, then generate ideas Output: Curated ideas/options with trade-offs, then offer to go deeper on the chosen one

Focused Mode

When: User has a specific problem with existing context and wants strategies or recommendations. Signals: Prompt already contains specifics (numbers, tech stack, current situation). User says "brainstorm วิธี...", "how to reduce...", "what's the best approach to..." Flow: Targeted discovery (0–2 questions) — only ask about genuine unknowns, skip what's already stated Output: Actionable strategies/recommendations with priorities and estimated impact

Workflow by Mode

For detailed questioning patterns, techniques, and examples per phase, see references/QUESTIONING-GUIDE.md


Prompt Mode — Full Discovery

The most thorough path. Use all phases when the user needs a well-crafted prompt.

Phase 1 — Receive: Acknowledge the request. Say something like: *"I'll help you craft that prompt. Let me ask a few questions to make it specific to your situation."*

Phase 2 — Goal: What does the user want the prompt to achieve? Get to a one-sentence goal with at least one measurable indicator. (1–2 questions)

Phase 3 — Direction: What must NOT happen? What approaches exist? Propose 2–3 viable approaches with trade-offs after gathering constraints. Lead with your recommendation. (1–2 questions + proposal)

Phase 4 — Reference (optional): Only if references would genuinely help (e.g., style/design requests). Ask if they have examples. If none and it would help, use WebSearch. If search returns poor or no results, tell the user and ask whether to proceed without references or try different search terms. Skip entirely for straightforward requests. (0–1 questions)

Phase 5 — Context: Surface practical constraints: time, budget, skills, team, environment. Flag contradictions with the goal gently. (1–2 questions)

Phase 6 — Criteria: Define what "good" means. Force-rank if more than 3 criteria. (1 question)

Phase 7 — Synthesize: Draft the improved prompt with this structure:

## Improved Prompt

[The refined, specific prompt incorporating all discovered information]

---

### Discovery Summary

**Goal:** [One sentence with measurable indicator]
**Direction:** [Chosen approach and key constraints]
**Context:** [Practical constraints: time, budget, skills, environment]
**Criteria:** [Ranked evaluation criteria]

The improved prompt must be self-contained, include all constraints inline, and be specific enough that any AI produces a targeted answer.

Present it and ask: *"Does this capture what you need? Anything to adjust?"* Iterate if needed.


Explore Mode — Light Discovery

For open-ended brainstorming where the user wants ideas, not a prompt.

Phase 1 — Receive + Quick Goal: Acknowledge, then ask ONE question combining goal + motivation via the questioning tool (see Tools table) with options like: Learning / Earning / Solving a problem / Building portfolio. (1 question)

Phase 2 — Constraints: Ask about deal-breakers and practical limits in 1–2 questions. Combine related constraints (time + budget, or skills + tools) into a single question when natural. (1–2 questions)

Phase 3 — Generate: Based on what you've learned, produce 5–8 concrete ideas organized by theme. Each idea should include:

  • What it is (one sentence)
  • Why it fits this user's constraints
  • One potential challenge

Phase 4 — Narrow: Ask which ideas resonate. Then offer:

  1. Go deeper on one idea (pivot to Prompt Mode or create a plan)
  2. Generate more ideas in a specific direction
  3. Done — take the ideas and go

Focused Mode — Targeted Discovery

For specific problems where the user already provided good context.

Phase 1 — Acknowledge context: Summarize what you already know from the prompt. Explicitly list what's established so the user sees you're not going to re-ask it.

Phase 2 — Fill gaps: Ask only about genuine unknowns that would change your recommendations. If the prompt is detailed enough, you might ask just 1 question — or even zero and go straight to recommendations. (0–2 questions)

Phase 3 — Strategize: Produce actionable recommendations:

  • Prioritized list (quick wins first, then bigger efforts)
  • Each item: what to do, estimated impact, effort level, risks
  • Clear "start here" recommendation

Phase 4 — Refine: Ask if anything needs adjustment. Offer to create an implementation plan via Plan subagent.


Next Step

After delivering the output (regardless of mode), offer next steps using AskUserQuestion (or ask_user / plain text if unavailable):

  • Create a Plan — Delegate to Plan subagent: Agent(subagent_type: "Plan") (Claude Code), task(agent_type: "general-purpose") with # Role: Planner block (Copilot), use_subagent (Kiro), or create the plan inline if none available
  • Go deeper — Continue exploring a specific aspect
  • Done — End the workflow

Handling Edge Cases

User wants to skip questions: Respect it. Produce the best output with what you have. Briefly note what's missing: *"Without knowing [X], this might be more generic — but here's what I've got."*

User says "I don't know": Offer 2–3 concrete options and let them react. Reactions reveal preferences without requiring expertise.

Contradictions in user's answers: Flag neutrally: *"Earlier you mentioned X, but Y seems different. Which should we prioritize?"*

Too broad for one session: Suggest splitting. Run the workflow for each piece.

Mode feels wrong mid-conversation: Switch. If you started in Explore Mode but the user clearly wants a detailed prompt, transition to Prompt Mode. No need to restart — carry forward what you've learned.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.3%
按下载量换算123

Claude

28.5%
按下载量换算99

Cursor

19.18%
按下载量换算67

Gemini CLI

9.22%
按下载量换算32

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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来源信息

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