Analytics Skills
Skills for analysts — dataset first looks, chart choice, analysis memos, SQL explanations, spreadsheet audits — that run on claude.ai, Claude Code, and Cowork.
Analysis Memo Writer
Turn findings and figures you supply into a stakeholder writeup with a fixed spine: headline finding, what the number is and what it is not, method in three lines, a mandatory caveats and limitations section, what would change the conclusion, and one recommended next step. Never invents a figure and never upgrades a correlation into a cause. Use when the analysis is done and has to survive being read by someone who was not in the query.
Chart Chooser
Pick the chart for a stated question and data shape and defend the choice: the recommendation, the reasoning, the alternatives rejected and why each fails this question, encoding rules for axis baseline, sorting, color, and labels, and generated code in the plotting library you name. Use when you know what you want the chart to say and need the one form that says it, not a gallery.
Dataset First Look
Turn a pasted CSV header with sample rows, a pasted table, or an attached data file into a fixed plain-language profile: shape, column inventory with inferred types, null and blank patterns, cardinality, suspicious columns, duplicate-key risk, outliers worth a look, and the questions to settle before analyzing. Use when a dataset just landed and you need to know what you are holding before you write a query.
Spreadsheet Formula Auditor
Audit pasted spreadsheet formulas or a described model for the errors that survive review: hardcoded values buried inside formulas, ranges that drift or truncate, one cell in a row that does not match its neighbors, circular references, sign errors, IFERROR masking a real failure, and volatile functions, returned as a fix list ordered by how much money the error moves. Use before a model that carries a real decision leaves your hands.
SQL Explainer
Walk through a pasted SQL query in execution order in plain language: what each step does, the grain of the result, where a join can fan rows out, which filters silently drop rows, and a ranked list of what could be wrong. Comprehension only, not performance tuning. Use when you inherited a query, are reviewing one before trusting its numbers, or have to explain it to someone who does not read SQL.