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Get Started Free →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.
.claude/skills/mohitagw15856-pivot-analysis-planner/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 87% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 107% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 30% | 0% |
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.
Ask for these if not provided:
The ask → 2–3 pivotable questions]
Tidy? · the fixes needed first, if any]
| Question | Rows | Columns | Values (aggregation + why) | Filters (stated) | |---|---|---|---|---|
What each layout will show · the drill path · the so-what test: which findings change a decision]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 11,824 | 19,713 | +67% | 1 | 1 | 0% | 1,888 | 2,945 | +56% | 0 | 0 | — |
case-01 | fail→pass | 43,172 | 24,579 | -43% | 1 | 1 | 0% | 5,424 | 4,312 | -21% | 0 | 0 | — |
case-02 | fail→fail | 49,425 | 28,366 | -43% | 1 | 1 | 0% | 6,141 | 4,698 | -23% | 0 | 0 | — |
case-03 | fail→fail | 49,567 | 27,007 | -46% | 1 | 1 | 0% | 6,834 | 4,793 | -30% | 0 | 0 | — |
case-05 | pass→pass | 14,802 | 19,663 | +33% | 1 | 1 | 0% | 1,678 | 3,227 | +92% | 0 | 0 | — |
case-06 | fail→pass | 19,379 | 22,750 | +17% | 1 | 1 | 0% | 1,971 | 3,883 | +97% | 0 | 0 | — |
case-07 | fail→pass | 17,391 | 22,595 | +30% | 1 | 1 | 0% | 1,994 | 3,738 | +87% | 0 | 0 | — |
case-08 | pass→pass | 14,681 | 19,027 | +30% | 1 | 1 | 0% | 1,867 | 3,281 | +76% | 0 | 0 | — |
case-09 | fail→fail | 15,407 | 22,096 | +43% | 1 | 1 | 0% | 2,015 | 3,907 | +94% | 0 | 0 | — |
case-10 | pass→pass | 19,319 | 27,468 | +42% | 1 | 1 | 0% | 2,059 | 4,103 | +99% | 0 | 0 | — |
case-11 | pass→pass | 13,127 | 14,629 | +11% | 1 | 1 | 0% | 1,375 | 2,956 | +115% | 0 | 0 | — |
case-12 | pass→pass | 16,249 | 16,111 | -1% | 1 | 1 | 0% | 1,752 | 3,473 | +98% | 0 | 0 | — |
case-13 | pass→pass | 20,231 | 20,105 | -1% | 1 | 1 | 0% | 2,224 | 3,466 | +56% | 0 | 0 | — |
case-14 | fail→pass | 15,203 | 14,872 | -2% | 1 | 1 | 0% | 1,739 | 3,598 | +107% | 0 | 0 | — |
case-15 | fail→pass | 116,802 | 21,356 | -82% | 1 | 1 | 0% | 2,515 | 3,265 | +30% | 0 | 0 | — |
case-16 | fail→fail | 19,943 | 18,363 | -8% | 1 | 1 | 0% | 1,918 | 2,685 | +40% | 0 | 0 | — |
case-17 | fail→pass | 16,421 | 22,270 | +36% | 1 | 1 | 0% | 1,689 | 3,367 | +99% | 0 | 0 | — |
case-18 | fail→fail | 12,168 | 9,795 | -20% | 1 | 1 | 0% | 1,560 | 2,570 | +65% | 0 | 0 | — |
case-19 | pass→fail | 22,153 | 21,938 | -1% | 1 | 1 | 0% | 2,479 | 3,291 | +33% | 0 | 0 | — |
case-20 | pass→pass | 12,646 | 9,427 | -25% | 1 | 1 | 0% | 1,322 | 2,201 | +66% | 0 | 0 | — |
case-21 | pass→pass | 6,475 | 20,432 | +216% | 1 | 1 | 0% | 1,221 | 2,574 | +111% | 0 | 0 | — |
case-22 | pass→pass | 25,960 | 43,368 | +67% | 1 | 1 | 0% | 3,491 | 5,185 | +49% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.