Agent skill
ds
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.
Install this agent skill to your Project
npx add-skill https://github.com/edwinhu/workflows/tree/main/skills/ds
SKILL.md
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:
- Check if
.planning/HANDOFF.mdexists - 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
- 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/constraints/ds-common-constraints.md for the full constraint index.
Load conventions for brainstorm phase:
Read ${CLAUDE_SKILL_DIR}/../../references/constraints/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
ASK 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
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
AskUserQuestionfor 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.mdwritten- 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 ────────────────────┘
- 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 (max 3 cycles)
- Phase 5: ds-verify - Check reproducibility and obtain user acceptance
No Pause After Brainstorm
| 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. </EXTREMELY-IMPORTANT>
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.
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