Agent-Skills.md

Agent Skills: Brainstorming (Questions Only)

Orchestrates 5-phase data analysis workflow with output-first verification.

data-analysisworkflowprocess-managementverificationphased-approach
analyticsID: edwinhu/workflows/ds

Repository

edwinhu/workflows
edwinhu

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pnpm dlx add-skill https://github.com/edwinhu/workflows/tree/HEAD/skills/ds

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skills/ds/SKILL.md

Skill Metadata

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."

Contents

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 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. </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 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]

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 |

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:

┌──────────────┐    ┌──────────┐    ┌──────────────┐    ┌───────────┐    ┌───────────┐
│ 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

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.