Data analysis

Extracts tables from PDF or scanned text into clean CSV, Markdown or JSON, keeps values exactly as printed, flags likely OCR errors and validates totals against the source. Use before analysis.

Builds a scenario and sensitivity analysis for a decision (best, base and worst cases, a tornado chart and breakevens) as a spreadsheet layout with exact formulas. Use before committing to a plan.

Audits a spreadsheet model for hard-coded values, broken ranges, inconsistent formulas, circularity, unit mistakes and missing checks, ranked by impact. Use before relying on someone else's sheet.

Designs a pivot table that answers one specific business question and gives exact click-by-click setup steps for Excel or Google Sheets. Use when you have a flat table and a question about it.

Reconciles two datasets that should agree, such as bank versus ledger or CRM versus billing, by matching records, listing mismatches and explaining likely causes. Use for month-end checks.

Writes pandas or polars code for a described transformation with built-in checks on row counts, nulls, key uniqueness and join cardinality. Use when reshaping, joining or aggregating data.

Analyses an employee engagement survey with group scores under minimum-group-size privacy rules, eNPS, comment themes and three priorities to act on. Use after an engagement or pulse survey closes.

Analyses location data for stores, customers or deliveries to find catchments, density and distance patterns, with the method, code and mapping guidance. Use for site, coverage or delivery questions.

Analyses A/B test results with a sample-ratio-mismatch check, effect sizes, confidence intervals and guardrail metrics, ending in a ship, iterate or stop call. Use when an experiment ends.

Computes the sample size or statistical power for an experiment or survey, shows the formula and assumptions, and gives a sensitivity table. Use before launching an A/B test, study or survey.

Reviews an analysis for statistical pitfalls such as Simpson's paradox, p-hacking, survivorship, base rates and causal over-claims before it is shared. Use as a pre-publication review.

Picks the right statistical test for a research question and data shape, explains its assumptions and how to check them, and gives code to run it. Use before testing a difference or relationship.

Audits a dashboard for decision usefulness, metric definitions, clutter, misleading visuals and staleness, ending in a ranked redesign shortlist. Use when a dashboard is ignored or distrusted.

Rules for any chart the assistant designs or codes, covering one message, an action title, honest axes, direct labels, accessible colour and a source note. Load whenever a chart or plot is made.

Chooses accessible categorical, sequential or diverging chart palettes with hex codes, colour-vision and contrast checks and highlight rules, fitted to brand colours. Use when colouring charts.

Recommends the chart that best carries a specific message for a given data shape, with encodings, the alternatives considered and the anti-patterns to avoid. Use before building a chart.

Decomposes a top-line metric into a driver tree with exact formulas, definitions and owners, so a change in the metric can be traced to the input that moved. Use for metric design and reviews.

Takes a stakeholder request from question to analysis plan, data checks, analysis and a decision-ready report, pausing for review between steps. Use when an analyst takes on a request.

Designs automation for a recurring report (sources, refresh, transformations, data checks, delivery) with tools matched to the team's skills. Use when a weekly or monthly report eats hours.

Compares performance across periods (YoY, MoM, like-for-like), handling trading days, holidays, seasonality and mix, and builds a variance story that adds up. Use before reporting a period change.