Interactive requirements discovery through Socratic dialogue and systematic exploration. Use when transforming ambiguous ideas into concrete specifications, validating concepts, or coordinating multi-persona analysis.
Transform ambiguous ideas into concrete specifications through structured exploration.
# 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
| 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 |
| 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 |
# 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"
)
| 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 |
# 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
'''
)
| 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) |
This skill does NOT require hard evidence. Focus on:
--strategy systematic)--strategy agile)--strategy enterprise)/sc:brainstorm "AI-powered analytics dashboard" --strategy systematic --depth deep
# Multi-persona analysis with comprehensive feasibility
/sc:brainstorm "real-time notifications" --strategy agile --parallel
# Parallel paths: frontend UX, backend architecture, security implications
/sc:brainstorm "enterprise data platform" --strategy enterprise --validate
# Compliance-aware exploration with security and devops input
Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Start voice calls via the OpenClaw voice-call plugin.
Notion API for creating and managing pages, databases, and blocks.
Gemini CLI for one-shot Q&A, summaries, and generation.
Category:developer