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Agent Skill · 研究检索

sc-brainstorm(头脑风暴)

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。

sc-brainstorm 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 47,712。

总安装 722周安装 31GitHub Stars 公开资料未说明下载 253核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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"

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/sc-brainstorm

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add tony363/superclaude --skill "sc-brainstorm";命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

sc-brainstorm 怎么安装?

在终端执行 npx skills add tony363/superclaude --skill "sc-brainstorm"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

sc-brainstorm 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

sc-brainstorm 需要什么权限?

权限级别是调用外部服务。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。

sc-brainstorm 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

sc-brainstorm 用的人多吗?

公开数据是累计安装 722 次,近一周安装 31 次,下载 253 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

sc-brainstorm 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
sc-brainstorm

头脑风暴 · 本页

722—调用外部服务MIT未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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

调用外部服务

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。目前只有一个公开来源,建议同时核对源仓库。

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