ds

name	ds
description	This skill should be used when the user asks to 'start data analysis', 'brainstorm analysis approach', 'plan a data project', 'clarify analysis requirements', or needs the full 5-phase data science workflow with output-first verification.
allowed-tools	Read, Grep, Glob, Bash, Skill, TodoWrite
hooks	{"PreToolUse":[{"matcher":"Bash","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-brainstorm-no-exploration-guard.py"}]}]}

Contents

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

Session Resume Detection

Before starting, check for an existing handoff:

1. Check if .planning/HANDOFF.md exists
2. If found: Read it and present to user:
   • Show the phase, task progress, and Next Action from the handoff
   • Ask: "Resume from handoff, or start fresh?"
   • If resume: skip to the recorded phase
   • If fresh: proceed with brainstorm
3. If not found: Proceed normally with Phase 1 (brainstorm)

Brainstorming (Questions Only)

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

Load shared enforcement first:

Read ${CLAUDE_SKILL_DIR}/../../references/ds-common-constraints.md for the full constraint index.

Load conventions for brainstorm phase: Read ${CLAUDE_SKILL_DIR}/../../references/ds-common-conventions.md for the full convention index. Read ${CLAUDE_SKILL_DIR}/../../references/conventions/ds-assumption-over-evidence.md Read ${CLAUDE_SKILL_DIR}/../../references/conventions/ds-deferred-verification.md Read ${CLAUDE_SKILL_DIR}/../../references/conventions/ds-impatience-over-process.md

## The Iron Law of DS Brainstorming

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

Smart-Discuss: Batch Ambiguities

When multiple analysis questions arise, batch them into ONE AskUserQuestion call:

Batched (fast — 1 round-trip):

AskUserQuestion(questions=[
  {"question": "Primary dataset?", "options": [{"label": "CRSP"}, {"label": "Compustat"}, {"label": "Both merged"}]},
  {"question": "Sample period?", "options": [{"label": "2000-2024"}, {"label": "2010-2024"}, {"label": "Custom"}]},
  {"question": "Frequency?", "options": [{"label": "Monthly"}, {"label": "Quarterly"}, {"label": "Annual"}]}
])

When to batch: After understanding the research question, if 3+ independent questions arise, batch them. When NOT to batch: If a question's answer changes what other questions to ask (e.g., dataset choice affects available variables).

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 .planning/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, discover and load the ds-plan skill for Phase 2:
>Read `${CLAUDE_SKILL_DIR}/../../skills/ds-plan/SKILL.md` and follow its instructions.

## Objective
[What question this analysis answers]

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

## Requirements

Assign each requirement a unique ID using `CATEGORY-NN` format (e.g., `DATA-01`, `VIZ-02`, `STAT-03`). Categories come from natural groupings in the analysis.

| ID | Requirement | Scope |
|----|-------------|-------|
| [CAT-01] | [Requirement 1] | v1 |
| [CAT-02] | [Requirement 2] | v1 |

Scope: `v1` = must complete, `v2` = nice to have, `out-of-scope` = explicitly excluded.

## Success Criteria
- [ ] [CAT-01] [Criterion]
- [ ] [CAT-02] [Criterion]

## 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]

Drive-Aligned Framing

Skipping the user interview is NOT HELPFUL — pattern-matching from similar requests produces wrong objectives, wasting the entire analysis. You are pattern-matching from similar-sounding requests, not understanding THIS specific analysis.

Drive-Aligned Framing (Consequences)

Shortcut	Consequence
Skipping user interview	You skipped questions because you thought it was faster. Wrong objectives mean the entire analysis is wasted — you were anti-helpful.
Not gathering sources	You assumed you knew the data. Your assumptions produce wrong results — your confidence was negligence.

Rationalization Table

Excuse	Reality	Do Instead
"I already know what analysis is needed"	You're pattern-matching from similar-sounding requests, not understanding THIS one	Ask questions first
"The data will tell me what to do"	Data exploration without objectives is aimless — you'll profile everything and answer nothing	Define objectives first
"User seems impatient, skip to analysis"	Wrong results from skipped brainstorm waste more time than 3 questions	Ask the questions
"The request is clear enough"	Clear to YOU is not clear to the user — your assumptions ≠ their intent	Confirm with AskUserQuestion
"I'll refine objectives as I go"	You'll commit to an approach and rationalize the objective to fit	Lock objectives before exploring

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

Gate: Exit Brainstorm

Checkpoint type: human-verify (SPEC.md content is machine-verifiable)

Before transitioning to ds-plan, execute this gate:

1. IDENTIFY → SPEC.md exists at `.planning/SPEC.md`
2. RUN      → Read(".planning/SPEC.md")
3. READ     → Verify it contains: Objectives, Data Sources, Success Criteria sections
4. VERIFY   → User has confirmed the objectives (not just agent self-assessment)
5. CLAIM    → Only proceed to ds-plan if ALL checks pass

If ANY check fails, do NOT proceed. Fix the gap first.

Self-assessment is not user confirmation. If the user hasn't explicitly approved the objectives, you haven't finished brainstorm.

Output

Declare brainstorm complete when:

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

Workflow Context

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

┌──────────────┐    ┌──────────┐    ┌──────────────┐    ┌───────────┐    ┌───────────┐
│ ds-brainstorm│───→│ ds-plan  │───→│ ds-implement │───→│ ds-review │───→│ ds-verify │
│  SPEC.md     │    │ PLAN.md  │    │ LEARNINGS.md │    │ APPROVED? │    │ COMPLETE? │
└──────────────┘    └──────────┘    └──────────────┘    └─────┬─────┘    └─────┬─────┘
                                         ↑                    │                │
                                         └── CHANGES REQ'D ───┘                │
                                         ↑                                     │
                                         └──── NEEDS WORK ────────────────────┘

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 (max 3 cycles)
5. Phase 5: ds-verify - Check reproducibility and obtain user acceptance

No Pause After Brainstorm

**After user confirms objectives, IMMEDIATELY proceed to ds-plan. Do NOT ask "should I continue?" or "ready to proceed?"**

Thought	Reality
"Should I ask if they want to continue?"	User already confirmed objectives. Asking again is stalling.
"Let me summarize what we agreed on"	SPEC.md IS the summary. Repeating it wastes context.
"Natural stopping point"	The workflow is sequential. Brainstorm done = plan starts. No gap.

Your pause is procrastination disguised as courtesy. The user confirmed — move.

Phase Complete

After completing brainstorm, dispatch the spec reviewer before proceeding:

Phase 1: Brainstorm -> SPEC.md written
  -> Dispatch ds-spec-reviewer subagent
  -> If APPROVED -> proceed to ds-plan
  -> If ISSUES_FOUND -> fix SPEC.md -> re-dispatch reviewer (max 5 iterations)

Step 1: Discover and load the spec reviewer skill: Read ${CLAUDE_SKILL_DIR}/../../skills/ds-spec-reviewer/SKILL.md and follow its instructions.

Step 2: Only after reviewer returns APPROVED, discover and load the next phase: Read ${CLAUDE_SKILL_DIR}/../../skills/ds-plan/SKILL.md and follow its instructions.

Fallback (if Read fails): /ds-plan

CRITICAL: Do not skip to analysis implementation. Phase 2 profiles data and breaks down the analysis into discrete, manageable tasks. CRITICAL: Do not skip spec review. An unreviewed spec means profiling the wrong data and planning the wrong analysis.