Agent skill
variance-analysis
Analyze period-over-period financial variance across channels
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/variance-analysis-jocko-fuel-cowork-plugins
SKILL.md
You are helping the finance team understand period-over-period financial variances.
IMPORTANT: Before doing anything else, use the ToolSearch tool with query +snowflake to load the snowflake MCP tools. All tools below are prefixed with mcp__snowflake__ (e.g., mcp__snowflake__get_pnl_summary).
Follow these steps:
Step 1: Define Comparison
Ask the user:
- Metric focus: Revenue, COGS, margin, or full P&L?
- Current period: Which period to analyze? (e.g., "this month", "Q1 2026")
- Comparison period: What to compare against? (e.g., "last month", "same month last year")
- Channel: Specific channel or all?
Step 2: Pull Data for Both Periods
Use mcp__snowflake__get_pnl_summary for each period. Also use mcp__snowflake__get_channel_revenue if channel-level revenue detail is needed. Use mcp__snowflake__get_unit_economics for per-unit variance.
Step 3: Calculate Variances
For each metric, calculate:
- Absolute variance — current period minus comparison period
- Percentage variance — (current - comparison) / comparison * 100
- Direction — favorable or unfavorable
Step 4: Root Cause Analysis
For the largest variances, investigate:
- Is the variance driven by volume changes or price/cost changes?
- Which channels or products are the biggest contributors?
- Are there one-time items distorting the comparison?
- Delegate to the
forecast-root-cause-analyzeragent for deeper analysis if needed
Step 5: Present Results
Format as a variance report:
- Summary table with current, prior, and variance columns
- Top 3-5 drivers of the variance
- Favorable vs unfavorable breakdown
- Recommendations or areas requiring attention
Step 6: Follow-Up
Offer:
- P&L report —
/jf-financial-analyst:pnl-report - Scenario modeling —
/jf-financial-analyst:scenario-model - Demand forecast —
/jf-financial-analyst:forecast-demand
Error Handling
- If Snowflake MCP is unavailable, inform the user and suggest checking the HORIZON_SNOWFLAKE_TOKEN
- If comparison period data is incomplete, note which metrics can and cannot be compared
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