/variance-analysis
Use to attribute forecast vs actual deltas and recommend remediation
$ npx -y skills add gtmagents/gtm-agents --skill variance-analysis --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/variance-analysis
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use to attribute forecast vs actual deltas and recommend remediation
SKILL.md
variance-analysis.SKILL.mdname: variance-analysis description: Use to attribute forecast vs actual deltas and recommend remediation actions.
Revenue Variance Analysis Skill
When to Use
- Preparing forecast reviews or board updates that require variance explanations.
- Investigating misses/exceeds across segments, products, or channels.
- Prioritizing remediation plays tied to specific variance drivers.
Framework
1. **Driver Taxonomy** – classify deltas into volume, conversion, price/mix, churn, expansion, currency. 2. **Attribution Logic** – define formulas for each driver and maintain consistent baselines. 3. **Root Cause Layer** – connect drivers to operational issues (pipeline quality, capacity, enablement, macro). 4. **Action Mapping** – translate each root cause into specific plays with owners and expected impact. 5. **Feedback Loop** – update forecasting assumptions once variance is understood.
Templates
- Variance waterfall chart setup instructions.
- Driver worksheet (metric → delta → driver → root cause → owner → due date).
- Remediation tracker with status and forecast impact.
Tips
- Keep a glossary so stakeholders interpret drivers consistently.
- Combine quantitative attribution with qualitative context from GTM leaders.
- Feed learnings back to `forecast-modeling` to tighten assumptions next cycle.
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