Variance Analysis
SkillCommerce & financeLets your agent break down financial variances between actuals, budget, forecast, and prior periods into quantified drivers with commentary.
Use Variance Analysis in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Variance Analysis skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Decompose actual, budget, forecast, and prior-period financial variances into quantified drivers and management commentary. Use for price-volume-mix, rate, headcount, spend, margin, and waterfall analysis.
What this skill tells your AI
The instructions your AI receives, as published by rongxinzy/rongxinai in SKILLs/zhiyuan-expert-manager/presets/finance-accounting-expert/skills/variance-analysis/SKILL.md and read by ahel’s review.
Prepare the comparison
Confirm entity, account hierarchy, period, currency, unit, actual/budget/forecast versions, sign convention, and materiality. Validate that compared datasets use consistent scope and mappings.
Calculate absolute variance = actual - comparison and percentage variance using a documented denominator. Treat zero or sign-changing denominators explicitly rather than presenting misleading percentages.
Decompose drivers
- Price/volume: volume effect = (actual volume - baseline volume) x baseline price; price effect = (actual price - baseline price) x actual volume.
- Mix/rate: quantify shifts among products, customers, channels, regions, grades, or contract types.
- Headcount: separate headcount, compensation rate, mix, hiring timing, attrition, bonus, and benefit effects.
- Spend: separate fixed, volume-driven, discretionary, contractual, one-time, foreign-exchange, and timing effects.
The starting value plus all signed drivers must equal the ending value. Show any residual as an unresolved item, not “other” without explanation.
Narrative
For each material variance state:
- amount, percentage, favorable/unfavorable status, comparison, and period;
- quantified primary and offsetting drivers;
- the business cause and evidence;
- whether the effect is timing, one-time, structural, or uncertain;
- outlook, action, owner, and forecast implication.
Avoid circular statements such as “revenue increased because revenue was higher.” Distinguish proven cause from plausible hypothesis.
Waterfall and output
Limit a waterfall to a decision-useful set of drivers, verify it reconciles, and accompany it with a driver table. Rank follow-up by absolute impact, unexpected direction, recurrence, trend, controllability, and materiality. State all data limitations and required review.
Signals
- GitHub stars
- 154
- Forks
- 3
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
variance-analysis-rongxinzy- Source
- github.com/rongxinzy/rongxinai
github.com/rongxinzy/rongxinai
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