Prompt

Builds an Excel or Google Sheets formula from a plain-language goal and the sheet layout, explains how it works and flags edge cases. Use when you know the result you want but not the formula.

Decides whether extreme values in an analysis are errors or real, sets a rule you can defend, and reports results with and without them using robust methods where they fit.

Plans anonymisation or pseudonymisation of a dataset before sharing, classifying identifiers, choosing techniques and assessing re-identification and residual risk. Use before data leaves your team.

Decides whether non-normal data actually matters for the planned analysis, using plots on the right quantity, and picks a fix (do nothing, transform, robust errors, bootstrap or nonparametric).

Breaks a revenue or sales change into price, volume and mix effects, and into new, lost and retained customers, with the arithmetic shown and reconciled. Use to explain why revenue moved.

Finds which behaviours and attributes predict churn in customer data, simple comparisons first and a model only if justified, with an action and a test per driver. Use at subscription businesses.

Writes SQL for activation rate and ordered conversion funnels from an event table, with conversion windows, strict step order, time zones, internal users, bots and late-arriving events handled.

Reviews someone else's forecast or projection by surfacing its hidden assumptions, testing them against base rates and capacity limits, and finding the few that drive the result.

Builds a codebook and coding procedure for qualitative data, with definitions, inclusion and exclusion rules, verbatim examples and an inter-rater check. Use before coding interviews or open text.

Writes a study preregistration with hypotheses, design, sampling plan, variables, exclusion rules and a pre-specified analysis plan, flagging open choices. For OSF or AsPredicted users.

Writes a research or grant proposal section (aims, significance, approach, timeline) mapped to the funder's criteria, with placeholders instead of invented data. Use for grant or thesis proposals.

Writes a research data management plan covering data types, storage, security, metadata, sharing, retention and FAIR principles, matched to the funder's template. For grant applicants.

Turn user needs into a business model and low-cost experiments.

Compare options, impacts, risks and next steps.

Coordinate resources, schedules, responsibilities and contingencies.

A customer reply, internal issue summary, routing tags and follow-up actions.

FAQ entries, search tags, documentation gaps and maintenance plan.

A customer response, resolution path, internal actions and escalation criteria.

Discovery questions, a fit matrix, follow-up email and next actions.

An executive summary, scope of work, phased plan and confirmation checklist.

A package comparison, hypothesis list, sensitivity analysis and validation plan.

A work breakdown, milestones, ownership table and schedule risks.

A risk register, early warnings, response priorities and review cadence.

A process-flow description, SOP, checklist and exception handling.