SQL query generator from a plain-English request
Converts a natural-language data requirement plus a table schema into a syntactically correct SQL query for a named database, with its assumptions stated up front.
Review a chart's scales, labels, aggregation and claims, separating visual issues from data questions that need verification.
No public result for version 1 yet
Version 1 · 3 variables · 3 required
Fill in the values below — the customized prompt updates as you type. marks a required value.
Who reads the chart and the decision it supports. Default: Operations managers comparing monthly support performance.
Attach a chart or describe its actual scales, labels and values. Default: Illustrative specification: vertical bars for January 82% and February 86%; y-axis spans 80%-90%; title says Performance doubled; no sample sizes shown.
Definitions, denominators, source data and intended claim. Default: Metric is tickets resolved within one business day. January had 100 tickets and February 50. The intended question is whether resolution improved.
Critique this existing chart for Operations managers comparing monthly support performance..
Chart image or precise specification: Illustrative specification: vertical bars for January 82% and February 86%; y-axis spans 80%-90%; title says Performance doubled; no sample sizes shown.
Underlying metric definitions and intended claim: Metric is tickets resolved within one business day. January had 100 tickets and February 50. The intended question is whether resolution improved.
Check whether the encoding supports the intended comparison. Examine axis ranges, baselines, scales, ordering, units, denominators, aggregation, uncertainty and missing groups where visible. Identify labels or color choices that could obscure meaning.
For each material issue, cite the visible feature, explain the likely misreading, and propose a minimal correction. Separate what can be seen from what requires the data or calculation logic; do not infer a truncated axis or missing observations without evidence.
Give a revised title and annotation plan that match the supported claim. End with the two highest-value checks before publication. Do not declare a chart statistically valid or accessible from an incomplete description.No public examples yet for version 1
Converts a natural-language data requirement plus a table schema into a syntactically correct SQL query for a named database, with its assumptions stated up front.
No public results yet
Check join cardinality and aggregation grain to explain duplicate rows or double-counted measures before changing a reporting query.
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Specify temporal evaluation, leakage checks, baselines and business-relevant metrics before judging a forecasting model.
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Label supplied comments consistently while preserving mixed opinions, unclear cases and the exact words supporting each label.
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Produce a deterministic transformation from repeated measure columns to tidy rows with explicit keys, types and missing-value rules.
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Specify cohort membership, return behavior and observation maturity so retention percentages remain comparable.
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