**TL;DR** — Financial Analyst ingests raw financial data and produces structured analytical output: metric decomposition, scenario models, variance analysis, and board-ready narratives — the work that usually takes an analyst two days, done in minutes.
1. **Define your metric schema** — Map your KPI definitions (ARR, NDR, GMV, CAC payback) into the skill's working vocabulary. This ensures the skill speaks your business's language, not generic finance.
2. **Feed it your standard charts and formatting** — Provide your board deck templates and chart styles. The skill outputs into your structure, not generic output shapes.
3. **Set scenario guardrails** — Define the range bounds for your key assumptions (growth rate, churn, pricing). The skill will flag extrapolations that exceed these ranges without calling them errors.
**Example Prompt:**
Upload our Q1 P&L (revenue $4.2M, gross margin 61%, OpEx $3.8M) and cap table (Series A @ $12M post-money, 18% dilution pool). Generate: (1) a variance analysis vs. Q1 plan, (2) a 12-month cash runway projection under three scenarios (base, bull, bear), and (3) a board-ready metric summary with the three slides formatted per our standard deck template.
| Pros | Cons |
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| Compresses financial analysis from days to minutes | Input data must be structured — messy exports produce unreliable output |
| Eliminates spreadsheet modeling errors | Financial projections still require human business judgment |
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| Generates board-ready narrative from raw data | May require CFO sign-off before investor-facing distribution |
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| Handles cap table math with audit-level precision | Scenario modeling quality depends on assumption inputs |
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