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name: ds-brainstorm description: "This skill should be used when the user asks to "start a data science project", "brainstorm analysis", "plan a data analysis", or wants to clarify analysis requirements. REQUIRED Phase 1 of /ds workflow. Uses Socratic questioning to clarify goals, data sources, and constraints."

Contents

• The Iron Law of DS Brainstorming
• What Brainstorm Does
• Critical Questions to Ask
• Process
• Red Flags - STOP If You're About To
• Output

Brainstorming (Questions Only)

Refine vague analysis requests into clear objectives through Socratic questioning. NO data exploration, NO coding - just questions and objectives.

ASK QUESTIONS BEFORE ANYTHING ELSE. This is not negotiable.

Before loading data, before exploring, before proposing approaches, you MUST:

1. Ask clarifying questions using AskUserQuestion
2. Understand what the user actually wants to learn
3. Identify data sources and constraints
4. Define success criteria
5. Only THEN propose analysis approaches

STOP - You're about to load data or explore before asking questions. Don't do this.

What Brainstorm Does

DO	DON'T
Ask clarifying questions	Load or explore data
Understand analysis objectives	Run queries
Identify data sources	Profile data (that's /ds-plan)
Define success criteria	Create visualizations
Ask about constraints	Write analysis code
Check if replicating existing analysis	Propose specific methodology

Brainstorm answers: WHAT and WHY Plan answers: HOW (data profile + tasks) (separate skill)

Critical Questions to Ask

Data Source Questions

• What data sources are available?
• Where is the data located (files, database, API)?
• What time period does the data cover?
• How frequently is the data updated?

Objective Questions

• What question are you trying to answer?
• Who is the audience for this analysis?
• What decisions will be made based on results?
• What would a successful outcome look like?

Constraint Questions

• Are you replicating an existing analysis? (Critical for methodology)
• Are there specific methodologies required?
• What is the timeline for this analysis?
• Are there computational resource constraints?

Output Questions

• What format should results be in (report, dashboard, model)?
• What visualizations are expected?
• How will results be validated?

Process

1. Ask Questions First

Employ AskUserQuestion immediately:

• One question at a time - never batch
• Multiple-choice preferred - easier to answer
• Focus on: objectives, data sources, constraints, replication requirements

2. Identify Replication Requirements

CRITICAL: Ask early if replicating existing work:

AskUserQuestion:
  question: "Are you replicating or extending existing analysis?"
  options:
    - label: "Replicating existing"
      description: "Must match specific methodology/results"
    - label: "Extending existing"
      description: "Building on prior work with modifications"
    - label: "New analysis"
      description: "Fresh analysis, methodology flexible"

When replicating:

• Obtain reference to original (paper, code, report)
• Document exact methodology requirements
• Define acceptable deviation from original results

3. Propose Approaches

After objectives are clear:

• Propose 2-3 different approaches with trade-offs
• Lead with recommendation (mark as "Recommended")
• Use AskUserQuestion for the user to select the preferred approach

4. Write Spec Doc

After selecting an approach:

• Write to .claude/SPEC.md
• Include: objectives, data sources, success criteria, constraints
• NO implementation details - reserve those for /ds-plan

# Spec: [Analysis Name]

> **For Claude:** After writing this spec, use `Read("${CLAUDE_PLUGIN_ROOT}/lib/skills/ds-plan/SKILL.md")` for Phase 2.

## Objective
[What question this analysis answers]

## Data Sources
- [Source 1]: [location, format, time period]
- [Source 2]: [location, format, time period]

## Success Criteria
- [ ] Criterion 1
- [ ] Criterion 2

## Constraints
- Replication: [yes/no - if yes, reference source]
- Timeline: [deadline]
- Methodology: [required approaches]

## Chosen Approach
[Description of selected approach]

## Rejected Alternatives
- Option B: [why rejected]
- Option C: [why rejected]

Red Flags - STOP If You Catch Yourself Doing This:

Action	Why It's Wrong	Do Instead
Loading data	You're exploring before understanding goals	Ask what the user wants to learn
Running describe()	You're profiling data when that's for /ds-plan	Finish defining objectives first
Proposing specific models	You're jumping to HOW before clarifying WHAT	Define success criteria first
Creating task lists	You're planning before objectives are clear	Complete brainstorm first
Skipping replication question	You might miss critical methodology constraints	Always ask about replication upfront

Output

Declare brainstorm complete when:

• Analysis objectives clearly understood
• Data sources identified
• Success criteria defined
• Constraints documented (especially replication requirements)
• Approach chosen from alternatives
• .claude/SPEC.md written
• User confirms ready for data exploration

Workflow Context

This skill is Phase 1 of the 5-phase /ds workflow:

1. Phase 1: ds-brainstorm (current) - Clarify objectives through Socratic questioning
2. Phase 2: ds-plan - Profile data and break analysis into tasks
3. Phase 3: ds-implement - Execute analysis tasks with output-first verification
4. Phase 4: ds-review - Review methodology, data quality, and statistical validity
5. Phase 5: ds-verify - Check reproducibility and obtain user acceptance

Phase Complete

After completing brainstorm, IMMEDIATELY invoke the next phase:

# Invoke Phase 2: Data profiling and task breakdown
/ds-plan

Or use the Skill tool directly:

Read("${CLAUDE_PLUGIN_ROOT}/lib/skills/ds-plan/SKILL.md")

CRITICAL: Do not skip to analysis implementation. Phase 2 profiles data and breaks down the analysis into discrete, manageable tasks.