First: Activate Workflow
Activate the ds workflow to enable workflow-specific hooks (data quality checks, output verification):
# Activate ds workflow and enable development mode for hook verification
python3 -c "
import sys
sys.path.insert(0, '\${CLAUDE_PLUGIN_ROOT}/hooks/scripts/common')
from session import activate_workflow, activate_dev_mode
activate_workflow('ds')
activate_dev_mode()
print('✓ DS workflow activated')
"
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.
<EXTREMELY-IMPORTANT> ## The Iron Law of DS BrainstormingASK QUESTIONS BEFORE ANYTHING ELSE. This is not negotiable.
Before loading data, before exploring, before proposing approaches, you MUST:
- Ask clarifying questions using AskUserQuestion
- Understand what the user actually wants to learn
- Identify data sources and constraints
- Define success criteria
- Only THEN propose analysis approaches
STOP - You're about to load data or explore before asking questions. Don't do this. </EXTREMELY-IMPORTANT>
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
AskUserQuestionfor 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 `Skill(skill="workflows:ds-plan")` 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.mdwritten- User confirms ready for data exploration
Workflow Context
This skill is Phase 1 of the 5-phase /ds workflow:
- Phase 1: ds-brainstorm (current) - Clarify objectives through Socratic questioning
- Phase 2: ds-plan - Profile data and break analysis into tasks
- Phase 3: ds-implement - Execute analysis tasks with output-first verification
- Phase 4: ds-review - Review methodology, data quality, and statistical validity
- 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:
Skill(skill="workflows:ds-plan")
CRITICAL: Do not skip to analysis implementation. Phase 2 profiles data and breaks down the analysis into discrete, manageable tasks.