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sc-brainstorm头脑风暴

Agent Skill

sc-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

722

周安装

31

GitHub Stars

公开资料未说明

下载量

253
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

MIT

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:sc-brainstorm(头脑风暴)
来源仓库:https://github.com/tony363/superclaude
仓库路径:skills/sc-brainstorm
安装命令:
npx skills add tony363/superclaude --skill "sc-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。

AgentSkills.tonpx skills
npx skills add tony363/superclaude --skill "sc-brainstorm"

简介

sc-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。

SKILL.md

Brainstorming & Requirements Discovery Skill

Transform ambiguous ideas into concrete specifications through structured exploration.

Quick Start

# Basic brainstorm
/sc:brainstorm [topic]

# Deep systematic exploration
/sc:brainstorm "AI project management tool" --strategy systematic --depth deep

# Parallel exploration with multiple personas
/sc:brainstorm "real-time collaboration" --strategy agile --parallel

Behavioral Flow

  1. Explore - Transform ambiguous ideas through Socratic dialogue
  2. Analyze - Coordinate multiple personas for domain expertise
  3. Validate - Apply feasibility assessment across domains
  4. Specify - Generate concrete specifications
  5. Handoff - Create actionable briefs for implementation

Flags

FlagTypeDefaultDescription
--strategystringsystematicsystematic, agile, enterprise
--depthstringnormalshallow, normal, deep
--parallelboolfalseEnable parallel exploration paths
--validateboolfalseInclude feasibility validation

Personas Activated

  • architect - System design and technical feasibility
  • analyzer - Requirements analysis and complexity assessment
  • frontend - User experience and interface considerations
  • backend - API and data architecture
  • security - Security requirements and compliance
  • devops - Infrastructure and deployment considerations
  • project-manager - Timeline and resource planning

MCP Integration

PAL MCP (Collaborative Intelligence)

ToolWhen to UsePurpose
mcp__pal__consensusConflicting prioritiesMulti-model resolution of trade-offs
mcp__pal__chatBrainstormingCollaborative idea exploration with external model
mcp__pal__thinkdeepComplex problemsMulti-stage deep analysis
mcp__pal__plannerSolution designSequential planning with branching
mcp__pal__challengeValidate ideasForce critical thinking on proposed solutions

PAL Usage Patterns

# Consensus on conflicting priorities
mcp__pal__consensus(
    models=[
        {"model": "gpt-5.2", "stance": "for", "stance_prompt": "Prioritize user experience"},
        {"model": "gemini-3-pro", "stance": "against", "stance_prompt": "Prioritize technical simplicity"},
        {"model": "deepseek", "stance": "neutral"}
    ],
    step="Evaluate: Should we use real-time sync or eventual consistency?"
)

# Deep exploration of complex idea
mcp__pal__thinkdeep(
    step="Exploring AI-powered analytics dashboard concept",
    hypothesis="Users need predictive insights, not just historical data",
    confidence="medium",
    focus_areas=["user_needs", "technical_feasibility", "market_fit"]
)

# Collaborative brainstorming
mcp__pal__chat(
    prompt="Help me explore innovative approaches for real-time collaboration in document editing",
    model="gpt-5.2",
    thinking_mode="high"
)

# Challenge assumptions
mcp__pal__challenge(
    prompt="We assume users want AI-generated summaries. Is this assumption valid?"
)

# Plan solution architecture
mcp__pal__planner(
    step="Planning architecture for real-time notification system",
    step_number=1,
    total_steps=4,
    is_branch_point=True,
    branch_id="websocket-approach"
)

Rube MCP (Research & Persistence)

ToolWhen to UsePurpose
mcp__rube__RUBE_SEARCH_TOOLSMarket researchFind web search, competitor analysis tools
mcp__rube__RUBE_MULTI_EXECUTE_TOOLDocumentationSave ideas to Notion, share in Slack
mcp__rube__RUBE_CREATE_UPDATE_RECIPEWorkflowsSave brainstorming processes
mcp__rube__RUBE_REMOTE_WORKBENCHData analysisAnalyze market data, user research

Rube Usage Patterns

# Research market and competitors
mcp__rube__RUBE_SEARCH_TOOLS(queries=[
    {"use_case": "web search", "known_fields": "query:AI analytics dashboard competitors 2025"}
])

# Document brainstorming session
mcp__rube__RUBE_MULTI_EXECUTE_TOOL(tools=[
    {"tool_slug": "NOTION_CREATE_PAGE", "arguments": {
        "title": "Brainstorm: AI Analytics Dashboard",
        "content": "## Key Ideas\n- Predictive insights\n- Natural language queries\n\n## Decisions\n- Real-time sync chosen over eventual consistency"
    }},
    {"tool_slug": "SLACK_SEND_MESSAGE", "arguments": {
        "channel": "#product",
        "text": "New brainstorm session documented: AI Analytics Dashboard"
    }}
])

# Create user research tasks
mcp__rube__RUBE_MULTI_EXECUTE_TOOL(tools=[
    {"tool_slug": "JIRA_CREATE_ISSUE", "arguments": {
        "project": "PROD",
        "summary": "User research: AI analytics preferences",
        "issue_type": "Task",
        "description": "Interview 10 users about analytics needs"
    }},
    {"tool_slug": "ASANA_CREATE_TASK", "arguments": {
        "name": "Competitor analysis: analytics dashboards",
        "project": "Research"
    }}
])

# Analyze existing user feedback
mcp__rube__RUBE_REMOTE_WORKBENCH(
    thought="Analyze user feedback data for patterns",
    code_to_execute='''
import json
# Load user feedback from file
feedback_data = json.load(open("/tmp/user_feedback.json"))
# Analyze with LLM
analysis, error = invoke_llm(f"Analyze this user feedback for analytics feature requests: {feedback_data[:5000]}")
output = {"analysis": analysis, "feedback_count": len(feedback_data)}
output
'''
)

Flags (Extended)

FlagTypeDefaultDescription
--pal-consensusboolfalseUse PAL consensus for trade-offs
--pal-deepboolfalseUse PAL thinkdeep for complex exploration
--researchboolfalseUse Rube for market/competitor research
--documentstring-Document to Rube (notion, confluence, google-docs)
--notifystring-Notify via Rube (slack, teams, email)

Evidence Requirements

This skill does NOT require hard evidence. Focus on:

  • Documenting exploration paths and decisions
  • Recording stakeholder input and priorities
  • Capturing specifications and requirements

Exploration Strategies

Systematic (--strategy systematic)

  • Structured question-driven discovery
  • Comprehensive domain coverage
  • Documentation-heavy approach

Agile (--strategy agile)

  • Rapid iteration cycles
  • User story focused
  • Minimal viable specification

Enterprise (--strategy enterprise)

  • Compliance and governance focus
  • Stakeholder alignment
  • Risk assessment integration

Examples

Product Discovery

/sc:brainstorm "AI-powered analytics dashboard" --strategy systematic --depth deep
# Multi-persona analysis with comprehensive feasibility

Feature Exploration

/sc:brainstorm "real-time notifications" --strategy agile --parallel
# Parallel paths: frontend UX, backend architecture, security implications

Enterprise Solution

/sc:brainstorm "enterprise data platform" --strategy enterprise --validate
# Compliance-aware exploration with security and devops input

Tool Coordination

  • Read/Write - Requirements documentation
  • TodoWrite - Exploration progress tracking
  • Task - Parallel exploration delegation
  • WebSearch - Market research and technology validation

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

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

29.16%
按下载量换算74

Claude Code

21.22%
按下载量换算54

Antigravity

14.95%
按下载量换算38

Gemini CLI

12.6%
按下载量换算32

windsurf

7.41%
按下载量换算19

trae

2.98%
按下载量换算8

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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