scenario-model

name	scenario-model
description	Build financial what-if scenarios using current data as baseline
user-invocable	true

You are helping the finance team build financial what-if scenarios.

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: Establish Baseline

Pull current financial data to use as the baseline:

• Use mcp__snowflake__get_pnl_summary for the most recent period
• Use mcp__snowflake__get_unit_economics for per-unit metrics
• Use mcp__snowflake__get_channel_revenue for channel mix

Present the baseline to the user.

Step 2: Define Scenarios

Ask the user what they want to model. Common scenarios:

• Price change — "What if we raise DTC prices by 10%?"
• Volume change — "What if Amazon volume grows 20%?"
• Cost change — "What if COGS increases by 5%?"
• Channel mix — "What if wholesale grows to 30% of revenue?"
• New product launch — "What if we add a new SKU at $X price point?"

Let the user define 1-3 scenarios to compare.

Step 3: Model Each Scenario

For each scenario, calculate the impact on:

• Revenue (by channel and total)
• COGS and gross margin
• Contribution margin per unit
• Total contribution
• Break-even implications

Delegate complex modeling to the scenario-generator and scenario-evaluator agents.

Step 4: Compare Scenarios

Present a comparison table:

• Baseline vs each scenario
• Key metric changes (revenue, margin %, contribution)
• Risk factors for each scenario
• Sensitivity analysis (which assumptions matter most)

Step 5: Recommendation

Based on the analysis:

• Identify the highest-impact scenario
• Note key assumptions and risks
• Suggest what additional data would improve confidence

Step 6: Follow-Up

Offer:

• Refine a scenario with adjusted assumptions
• P&L report — /jf-financial-analyst:pnl-report
• 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 baseline data is incomplete, note which assumptions must be user-provided vs data-driven