dataset-question-generator

name	dataset-question-generator
description	Generate fresh, visualisable data questions from raw datasets; reject stale prompts before charting.

Dataset Question Generator

Use this before analysis planning or charting. The job is to turn a raw dataset into a short ranked set of good questions.

Core sequence:

raw dataset → profile → signals → candidate questions → freshness filter → ranked prompts

This skill is deliberately upstream of karthik-analysis-planner, dataviz-selector, and karthik-data-visualization. Do not start with chart forms. Start with what the data makes worth asking.

Workflow

1. Inspect the data first. Do not brainstorm from the filename alone.
2. Profile the dataset: row count, row grain, date range, entities, measures, categories, missingness, and format breaks.
3. Identify visible signals: slope change, crossing, plateau, rebound, spike, seasonality, outlier, cluster, concentration, substitution, divergence, or denominator trap.
4. Generate 8-15 rough candidate questions.
5. Reject questions that are stale, obvious, non-measurable, or only restate column names.
6. Return the best 3-7 questions, ranked. Fewer is fine if only a few are good.

What to inspect

Minimum profile:

• Unit: what does one row represent?
• Coverage: time range, geography, entities, cohorts, currentness.
• Measures: counts, totals, shares, rates, rankings, amounts, durations, text fields.
• Dimensions: entity, segment, region, channel, cohort, status, category.
• Missingness: columns/periods/entities with suspicious gaps.
• Format breaks: old/new schemas, changed definitions, renamed categories, one-off shocks.
• Derived metrics: share, per-capita/per-unit, average size, rate, gap, index, concentration, volatility.

Karthik-style question patterns

Prefer questions with a visible comparison or mechanism:

• Has X actually changed, or has denominator Y changed?
• Is growth coming from more events, bigger events, or a change in mix?
• Which series has become boring/mature, and is that the interesting fact?
• Where is the gap widening or narrowing?
• Which entities are exceptions to the overall trend?
• Did an event or format break change the level, slope, or composition?
• Is the latest period unusual versus history, or only versus memory?
• Are totals driven by a few big entities or broad participation?
• Does the same story hold in value, volume, share, and average size?
• Where are things stuck, delayed, concentrated, or leaking?

Local analysis priors behind this:

• Bangalore weather: test lived disputes against historical normals and records.
• Bangalore wind/rain: abandon obvious but useless charts; ask a simpler mechanism question.
• Payments/demonetisation: separate volume, value, average size, and counterfactual trend.
• Elections: look for swings, regions, margins, corners, and seat-vote conversion - not just winners.
• Operations data: find bottlenecks, time-in-stage, delay cost, and peer benchmarks.
• Survey data: use ordered subgroup comparisons, concentration, and correlation structure.
• Urban morphology: compare measured features against terrain, history, or periphery - do not just map them.

Freshness filter

Before final output, test each candidate:

Criterion	Reject if weak
Visual contrast	no likely crossing, gap, slope change, outlier, distribution, or cluster
Freshness	obvious, dated, or already answered by the public narrative
Dataset support	needs external causal story not in the data
Denominator clarity	no clear unit or denominator
Karthik fit	dashboard-y, generic, or corporate-slop phrasing
Teachability	no useful lesson in chart choice or metric design

If a question sounds like "trend of X over time", rewrite it around the comparison: compared to what, split by whom, measured how, and why now?

Output modes

Questions only

Return only short questions, one per line. No chart notes.

Workshop seeds

### <question>
Use: <fields/entities>
Chart idea: <simple visual>
Why useful: <one sentence>
Watch out: <denominator/caveat>

Analysis-ready

- Question:
- Unit:
- Metric:
- Comparison:
- Visual:
- Caveat/falsifier:

Hard rules

• Do not answer questions before profiling.
• Do not overfit to column names; inspect values and ranges.
• Do not include stale defaults just because the domain suggests them.
• Do not recommend dashboards as the output. Recommend interpreted questions.
• Do not use causal words unless the comparison design supports them.
• Prefer fewer, sharper questions over a long generic list.