Data exploration
Exploratory analysis: profiling a dataset, finding patterns and anomalies, analyst SQL.
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- Analyse website analytics
Analyses website analytics (GA4 or similar) for traffic sources, landing pages, engagement and conversion, flags tracking problems first and gives prioritised actions. Use as a marketer or site owner.
- Classify text records
Classifies free-text records such as tickets, feedback or expenses into a given set of categories, with a confidence level and an explicit Other bucket, and returns a table.
- Reconcile two datasets
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.
- Analyse survey results
Analyses quantitative survey responses with cleaning, tabulation, cross-tabs and optional weighting, and states the caveats about sample and response bias. Use before reporting survey numbers.
- Data analyst
Acts as a data analyst who starts from the decision, sanity-checks data before trusting it and states uncertainty plainly. Use as a standing analyst persona or subagent for data questions.
- Deduplicate messy records
Plans and writes matching logic to deduplicate people, companies or products across messy records, with normalisation, blocking, fuzzy thresholds, merge rules and a review queue. Use for CRM cleanup.
- Explore a dataset
Runs a first-pass exploratory analysis of a dataset (column profiles, missingness, distributions, outliers) and lists the questions worth asking next. Use when you get new data.
- Find churn drivers
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.
- Segment customers
Proposes and builds a customer segmentation (RFM, rules or clustering) with interpretable segment profiles and a suggested action for each. Use to target retention, pricing or marketing work.
- Write a data request brief
Turns a vague stakeholder ask into a clear data request with the decision, exact metric definitions, filters, time range, format and deadline, plus open questions. Use when a data ask arrives vague.
- Write a dataframe transformation
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.
- Decompose a revenue change
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.
- Write an analysis plan
Writes an analysis plan before touching data, covering the decision, questions, metrics, data, method, comparisons, pitfalls and the result that would change the decision. Use when scoping a request.
- Review analytical SQL
Reviews an analytical SQL query for logic errors that give wrong numbers, such as join fan-out, misplaced filters, NULLs, double counting and date or time-zone boundaries. Use before sharing results.
- Build a cohort retention analysis
Builds a cohort retention analysis from event data (cohort definition, query or code, the retention triangle) and explains how to read it. Use to see whether newer customers stick around better.
- Run a market basket analysis
Runs market basket analysis on transactions to find products bought together, explains support, confidence and lift, and suggests bundles or placement to test. Use for retail and e-commerce.
- Detect anomalies in data
Finds anomalies in a metric or dataset with methods that fit its shape (thresholds, seasonality, robust z-scores), ranks them, and separates data errors from real events. Use when monitoring data.
- Answer a question with SQL
Turns a business question and a schema into an analytical SQL query, states the assumptions behind it and explains how to read the result. Use when you know the question but not the query.
Not: building pipelines or tuning queries (data).