Plan pivot-table analysis that answers the actual question — the question-to-layout mapping (rows, values, filters chosen on purpose), the data-shape check that pivots require, and the drill-down path from summary to so-what. Use when asked analyze this data with a pivot, what's driving the total, break this down by category and month, or my pivot shows nonsense. Produces the question decomposition, the pivot layout(s) with reasons, the data-shape fixes needed first, and the reading guide.
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Use pivot-analysis-planner to turn [business question] and [data columns plus sample] into pivot layouts for [required comparison], including data-shape fixes, justified aggregations and filters, drill-down steps, and a reading guide.
Required inputs
[business question]
[data columns plus sample]
[required comparison]
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SKILL.md
name: pivot-analysis-plannerdescription: "Plan pivot-table analysis that answers the actual question — the question-to-layout mapping (rows, values, filters chosen on purpose), the data-shape check that pivots require, and the drill-down path from summary to so-what. Use when asked analyze this data with a pivot, what's driving the total, break this down by category and month, or my pivot shows nonsense. Produces the question decomposition, the pivot layout(s) with reasons, the data-shape fixes needed first, and the reading guide."
Pivot Analysis Planner Skill
Pivot tables answer "what's driving X" in seconds — when the layout matches the question and the data is shaped right. Most pivot frustration is one of those two: a layout assembled by dragging until something looks meaningful, or data that isn't tidy (merged cells, subtotal rows baked in, one-column-per-month) feeding a pivot that double-counts. This skill works backwards from the question: decompose it, shape-check the data, choose the layout deliberately, and plan the drill-down — because the first pivot is the start of the analysis, not its output.
What This Skill Produces
The question decomposition — the vague ask ("analyze sales") turned into pivotable questions ("which product line drove the Q2 change, and is it price or volume?")
The shape check — the data problems that break pivots, found before the pivot finds them
The layout plan — rows/columns/values/filters per question, each placement with its reason
The reading guide — what the pivot will show, the drill-down path, and the so-what test
Required Inputs
Ask for these if not provided:
The question, pushed to specific: "analyze this" becomes 2–3 answerable questions — the decomposition is half the skill, and it needs the user's intent
The data's shape — columns and a sample; tidy (one row = one record, one column = one variable) or the fixes list gets written first
The comparison that matters — vs last period? vs plan? across segments? Comparisons decide the column dimension and whether calculated fields are needed
Framework: The Planning Rules
Question first, layout second: each pivotable question maps to a layout — "what drove the change" → dimension in rows, period in columns, delta readable across; "where is it concentrated" → dimension in rows, values sorted descending, running % if available. Dragging-until-interesting produces coincidences, not answers.
Tidy data or fix it first: pivots need one-row-one-record: unmerge cells, delete pre-baked subtotal rows (they double-count), unpivot month-columns into a date column (data-cleaning-pass handles the mess). Five minutes of shaping saves an hour of pivot mystery.
Values need their aggregation chosen, not defaulted: SUM for additive things, COUNT for events, AVERAGE almost never without a weight-check (the average-of-averages trap). Every value field's aggregation is a decision with a reason.
Filters are the scope contract: what's excluded (test rows, internal accounts, incomplete current month) gets decided and stated in the output — an unlabeled filtered pivot is how two people present different totals from one dataset.
Plan the drill, then the so-what: the summary pivot points somewhere; the plan names the next cut ("if the East drop is real, re-pivot East by product") and ends at the so-what test — a pivot finding that suggests no action or decision was trivia, and the reading guide says which findings would matter.
Output Format
Pivot Plan: [dataset] — question: [the real one]
Decomposition
[The ask → 2–3 pivotable questions]
Shape Check
[Tidy? · the fixes needed first, if any]
Layouts
Question
Rows
Columns
Values (aggregation + why)
Filters (stated)
Reading Guide
[What each layout will show · the drill path · the so-what test: which findings change a decision]
Quality Checks
Every layout traces to a decomposed question
The shape check ran before any layout advice
Every value field's aggregation has a reason; average-of-averages is guarded
Filters/exclusions are stated where the output will be shown
The drill-down path and so-what test exist — the pivot is a step, not the deliverable
Anti-Patterns
Do not drag until interesting — layouts follow questions or they follow noise
Do not pivot over baked-in subtotal rows — the double-count classic
Do not average averages — weight or don't
Do not present a filtered pivot without its scope note — that's how meetings get two truths
Do not stop at the summary — the first pivot locates the question; the drill answers it