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Decompose variances into drivers with narrative explanations and waterfall analysis
npx claudepluginhub fuww/knowledge-work-plugins<line item> <period> vs <comparison># Variance / Flux Analysis > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../CONNECTORS.md). **Important**: This command assists with variance analysis workflows but does not provide financial advice. All analyses should be reviewed by qualified financial professionals before use in reporting. Decompose variances into underlying drivers, provide narrative explanations for significant variances, and generate waterfall analysis. ## Usage ### Arguments - `area` — The area to analyze: - `revenue` — Revenue variance by stream, produ...
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If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Important: This command assists with variance analysis workflows but does not provide financial advice. All analyses should be reviewed by qualified financial professionals before use in reporting.
Decompose variances into underlying drivers, provide narrative explanations for significant variances, and generate waterfall analysis.
/flux <area> <period-comparison>
area — The area to analyze:
revenue — Revenue variance by stream, product, geography, customer segmentopex — Operating expense variance by category, department, cost centercapex — Capital expenditure variance vs budget by project and asset classheadcount — Headcount and compensation variance by department and role levelcogs or cost-of-revenue — Cost of revenue variance by componentgross-margin — Gross margin analysis with mix and rate effectsperiod-comparison — The periods to compare. Formats:
2024-12 vs 2024-11 — Month over month2024-12 vs 2023-12 — Year over year2024-Q4 vs 2024-Q3 — Quarter over quarter2024-12 vs budget — Actual vs budget2024-12 vs forecast — Actual vs forecast2024-Q4 vs 2024-Q3 vs 2023-Q4 — Three-way comparisonIf Google Sheets or BigQuery is connected:
If no data source is connected:
Connect Google Sheets or BigQuery to pull financial data automatically. To analyze manually, provide:
- Actual data for both comparison periods (at account or line-item detail)
- Budget/forecast data (if comparing to plan)
- Any operational metrics that drive the financial results (headcount, volumes, pricing, etc.)
VARIANCE SUMMARY: [Area] — [Period 1] vs [Period 2]
Period 1 Period 2 Variance ($) Variance (%)
-------- -------- ------------ ------------
Total [Area] $XX,XXX $XX,XXX $X,XXX X.X%
Break down the total variance into constituent drivers. Use the appropriate decomposition method for the area:
Revenue Decomposition:
Operating Expense Decomposition:
CapEx Decomposition:
Headcount Decomposition:
Generate a text-based waterfall showing how each driver contributes to the total variance:
WATERFALL: [Area] — [Period 1] vs [Period 2]
[Period 2 Base] $XX,XXX
|
|--[+] [Driver 1 description] +$X,XXX
|--[+] [Driver 2 description] +$X,XXX
|--[-] [Driver 3 description] -$X,XXX
|--[+] [Driver 4 description] +$X,XXX
|--[-] [Driver 5 description] -$X,XXX
|
[Period 1 Actual] $XX,XXX
Variance Reconciliation:
Driver 1: +$X,XXX (XX% of total variance)
Driver 2: +$X,XXX (XX% of total variance)
Driver 3: -$X,XXX (XX% of total variance)
Driver 4: +$X,XXX (XX% of total variance)
Driver 5: -$X,XXX (XX% of total variance)
Unexplained: $X,XXX (XX% of total variance)
--------
Total: $X,XXX (100%)
For each significant driver, generate a narrative explanation:
[Driver name] — [Favorable/Unfavorable] variance of $X,XXX (X.X%)
[2-3 sentence explanation of what caused this variance, referencing specific operational factors, business events, or decisions. Include quantification where possible.]
Outlook: [Whether this is expected to continue, reverse, or change in future periods]
If the decomposition does not fully explain the total variance, flag the residual:
Unexplained variance: $X,XXX (X.X% of total)
Possible causes to investigate:
- [Suggested area 1]
- [Suggested area 2]
- [Suggested area 3]
Ask the user for additional context on unexplained variances:
Provide:
For FashionUnited, use the following defaults:
Revenue Variance Drivers:
OpEx Variance Drivers:
Market-Level Analysis: Analyze revenue by market: Netherlands, Germany, UK, France, US, Other
FX Analysis: Isolate FX impact for:
Materiality Thresholds:
| Category | EUR Threshold | % Threshold |
|---|---|---|
| Total Revenue | EUR 10,000 | 5% |
| Revenue by Stream | EUR 5,000 | 10% |
| OpEx Category | EUR 5,000 | 15% |
| Individual Line | EUR 2,500 | 20% |
Currency: EUR