Brainstorming & Requirements Discovery Skill Skill

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

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

Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | --strategy | string | systematic | systematic, agile, enterprise | | --depth | string | normal | shallow, normal, deep | | --parallel | bool | false | Enable parallel exploration paths | | --validate | bool | false | Include 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)

| Tool | When to Use | Purpose | |------|-------------|---------| | mcp__pal__consensus | Conflicting priorities | Multi-model resolution of trade-offs | | mcp__pal__chat | Brainstorming | Collaborative idea exploration with external model | | mcp__pal__thinkdeep | Complex problems | Multi-stage deep analysis | | mcp__pal__planner | Solution design | Sequential planning with branching | | mcp__pal__challenge | Validate ideas | Force 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)

| Tool | When to Use | Purpose | |------|-------------|---------| | mcp__rube__RUBE_SEARCH_TOOLS | Market research | Find web search, competitor analysis tools | | mcp__rube__RUBE_MULTI_EXECUTE_TOOL | Documentation | Save ideas to Notion, share in Slack | | mcp__rube__RUBE_CREATE_UPDATE_RECIPE | Workflows | Save brainstorming processes | | mcp__rube__RUBE_REMOTE_WORKBENCH | Data analysis | Analyze 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)

| Flag | Type | Default | Description | |------|------|---------|-------------| | --pal-consensus | bool | false | Use PAL consensus for trade-offs | | --pal-deep | bool | false | Use PAL thinkdeep for complex exploration | | --research | bool | false | Use Rube for market/competitor research | | --document | string | - | Document to Rube (notion, confluence, google-docs) | | --notify | string | - | 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

Agent Skills: Brainstorming & Requirements Discovery Skill

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Skill Files