mcpField
S
sc-brainstorm

sc-brainstorm

github.com/tony363Development2099 installsUpdated 30w ago
Repository

About sc-brainstorm

sc-brainstorm is a skill for turning unclear ideas into requirements and implementation briefs through structured exploration. It supports multiple strategies, persona-based analysis, feasibility checks, and optional PAL and Rube MCP workflows.

Key features

  1. 01Structured idea exploration
  2. 02Systematic, agile, and enterprise strategies
  3. 03Parallel persona-based analysis
  4. 04Feasibility validation
  5. 05Optional research and documentation via MCP

Use cases

  1. 01Clarify an early product idea
  2. 02Explore feature requirements from multiple angles
  3. 03Assess feasibility and trade-offs
  4. 04Research a market and document findings

MCP support

The skill can use PAL MCP for collaborative exploration, analysis, planning, and trade-off resolution, and Rube MCP for research, data analysis, and documenting or sharing results.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tony363/superclaude --skill sc-brainstorm

After installing, type/sc-brainstormin Claude Code to confirm it loaded.

Details

From the project README

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

Comments

Markdown supported