data-outlier-finderlisted

# Data outlier finder ## What this skill does Identifies unusual values, unexpected patterns, and potential stories hidden in a dataset by systematically checking for statistical outliers and contextual anomalies. ## When to use this skill - You have a dataset and want to know where the stories are before you start reporting - An editor asks "what's surprising in this data?" and you need a structured answer - You are fact-checking a claim and want to verify whether the cited figure is genuinely unusual - You received a data dump (FOI response, leaked spreadsheet, public database) and need to triage it for newsworthy patterns ## What you need to provide **Required:** - The dataset or a representative sample (paste the data, describe the columns, or share summary statistics) - What the data measures and where it comes from **Optional:** - What you expect to find (so the assistant can flag deviations from your assumptions) - Known context that might explain outliers (e.g., "2020 data will be distorted by COVID") - Whether you want statistical outliers only or also contextual anomalies (values that are technically valid but editorially surprising) ## How the Assistant Approaches This 1. Identifies the key numeric columns and their expected ranges 2. Flags statistical outliers — values more than 2 standard deviations from the mean, or in the top/bottom 5% of the distribution 3. Checks for contextual anomalies — sudden changes between time periods, values that contradict known