FP&A Scenario Modeling
SkillCommerce & financeBuild structured 3-scenario financial models (Base / Bull / Bear) for business planning or investment decisions. Sensitize revenue and cost drivers across scenarios, produce P&L summaries, cash flow bridges, and executive narratives. Aligns with FP&A 2.0 and CIMA Management Accounting standards. Use when leadership needs to understand the financial impact range of a decision. Do not use for statutory financial reporting or audit-ready financials.
Use FP&A Scenario Modeling in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the FP&A Scenario Modeling 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.
What this skill tells your AI
The instructions your AI receives, as published by hoavdc/codexkit in skills/codexkit-fpa-scenario-modeling/SKILL.md and read by Ahel’s review.
Purpose
Transform business assumptions into a structured 3-scenario financial model that gives leadership a clear view of upside, baseline, and downside outcomes.
When to use
- annual or quarterly business planning cycle
- before a major investment decision (new product, M&A, expansion)
- when the CFO asks "what if revenue drops 20%?"
- presenting financial impact range to the board
When not to use
- statutory financial reporting (IFRS/GAAP compliance)
- audit-ready financial statements
- daily operational cost tracking
Inputs
- business model type (SaaS, retail, manufacturing, services)
- historical financials (last 2–3 years if available)
- key revenue drivers (pricing, volume, churn, expansion)
- key cost drivers (headcount, COGS, marketing spend, capex)
- specific decision context (what are we modeling?)
- time horizon (12 months, 3 years, 5 years)
Procedure
- Define 3 scenarios:
- Base Case: most likely outcome based on current trajectory
- Bull Case: upside scenario (best 30% of outcomes)
- Bear Case: downside/stress scenario (worst 20% of outcomes)
- Identify key variables to sensitize:
- Revenue-side: growth rate, gross margin, CAC, churn rate, ASP
- Cost-side: fixed cost base, variable cost as % revenue, capex, working capital (DSO, DPO, DIO)
- Build scenario assumption table — side-by-side comparison of all variables across 3 scenarios.
- Calculate P&L summary per scenario: Revenue → Gross Profit → EBITDA → Net Profit.
- Build cash flow bridge per scenario: Operating CF → Investing CF → Financing CF → Free CF.
- Calculate key metrics per scenario: EBITDA margin, ROI, Payback Period, Break-even point.
- Create sensitivity table — identify which single variable has the largest impact on outcome.
- Write executive narrative — "Under the base case, the business generates…" with clear decision implications.
Output
- scenario assumption table (side-by-side, 3 columns)
- P&L summary per scenario (Revenue → EBITDA → Net Profit)
- cash flow bridge per scenario
- key metrics comparison table
- sensitivity analysis (which variable moves the needle most)
- executive narrative (1–2 paragraphs per scenario)
Definition of done
- all 3 scenarios have distinct, justified assumptions
- P&L, cash flow, and key metrics are calculated for each scenario
- sensitivity analysis identifies the top 3 swing variables
- executive narrative is decision-ready (not just numbers)
Examples
- "Model 3 scenarios for our SaaS expansion into Southeast Asia over 3 years."
- "What happens to EBITDA if raw material costs increase 15% (bear case)?"
- "Build a scenario model for the board comparing organic growth vs. acquisition."
Quality Criteria
- Data sources and assumptions are explicitly stated
- Calculations are reproducible from provided inputs
- Visualizations or tables have clear labels, units, and time ranges
- Caveats and confidence levels are documented for estimates
Verification (4C)
| Check | Question |
|---|---|
| Correctness | Are formulas, aggregations, and statistical methods applied correctly? |
| Completeness | Does the analysis cover all requested metrics and time ranges? |
| Context-fit | Are the chosen metrics relevant to the business question being answered? |
| Consequence | If this data were used for a decision today, what blind spots remain? |
Edge Cases
- Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
- Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
- Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.
Changelog
- v1.0.0 — Initial release
Signals
- GitHub stars
- 25
- Forks
- 13
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
codexkit-fpa-scenario-modeling- Source
- github.com/hoavdc/codexkit
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