conducting-scenario-planning
Structures financial scenario analysis with assumption modeling, sensitivity testing, and decision frameworks. Use when modeling scenarios, testing assumptions, or evaluating strategic options.
Best use case
conducting-scenario-planning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures financial scenario analysis with assumption modeling, sensitivity testing, and decision frameworks. Use when modeling scenarios, testing assumptions, or evaluating strategic options.
Teams using conducting-scenario-planning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/conducting-scenario-planning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How conducting-scenario-planning Compares
| Feature / Agent | conducting-scenario-planning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures financial scenario analysis with assumption modeling, sensitivity testing, and decision frameworks. Use when modeling scenarios, testing assumptions, or evaluating strategic options.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Conducting Scenario Planning
Structures financial scenario analysis with assumption modeling, sensitivity testing, and decision frameworks for strategic and operational planning.
## When To Use
- Annual or quarterly budgeting cycles requiring upside/downside forecasts
- Evaluating capital allocation decisions (M&A, capex, new product lines)
- Stress-testing a business plan against macro or market disruptions
- Board or leadership presentations that need a range of financial outcomes
- Assessing go/no-go thresholds for strategic initiatives
- Contingency planning for supply chain, pricing, or demand shocks
## Inputs To Gather
- **Baseline financial model** — P&L, cash flow, and balance sheet projections with current assumptions
- **Key assumption variables** — the 5–10 drivers with the highest impact on outcomes (e.g., revenue growth rate, COGS %, customer churn, FX rates, interest rates)
- **Historical ranges** — actual min/max/mean values for each variable over a relevant lookback period (typically 3–5 years)
- **External benchmarks** — industry comps, analyst consensus, or macro forecasts that bound plausible ranges
- **Management hypotheses** — specific strategic actions or events to model (e.g., price increase, market entry, headcount freeze)
- **Decision criteria** — the metrics stakeholders will use to choose between scenarios (e.g., EBITDA margin, FCF breakeven, covenant compliance, IRR hurdle)
## Workflow
1. **Define scenario architecture**
- Select the scenario framework: discrete scenarios (base/bull/bear), Monte Carlo simulation, or combinatorial matrix
- For discrete scenarios, name and narratively define each case (e.g., "Bear: recession + 15% volume decline + 200bps rate increase")
- Identify which variables shift between scenarios and which remain constant
2. **Set assumption ranges**
- For each key variable, assign a value per scenario or a probability distribution
- Document the source and rationale for every assumption (historical data, management estimate, third-party forecast)
- Flag any assumption lacking empirical support with [VERIFY]
3. **Build scenario outputs**
- Run each scenario through the financial model to produce projected P&L, cash flow, and balance sheet
- Calculate decision-relevant metrics: revenue, EBITDA, net income, FCF, leverage ratios, liquidity runway, ROI/IRR
- Capture the delta vs. baseline for each metric to highlight scenario impact
4. **Perform sensitivity analysis**
- Isolate individual variables via one-at-a-time sensitivity (tornado chart)
- Identify breakeven thresholds — the variable value at which a key metric crosses a critical boundary (e.g., "revenue must exceed $X for covenant compliance")
- Run two-way sensitivity tables for the top correlated variable pairs
5. **Assess probability and risk**
- Assign subjective or data-driven probability weights to each scenario if stakeholders require an expected-value view
- Map scenarios to a risk matrix (likelihood × financial impact)
- Identify tail-risk scenarios that, while low-probability, would be existential or covenant-breaking
6. **Develop decision framework**
- Link each scenario outcome to a recommended action or contingency trigger
- Define early-warning indicators that signal which scenario is materializing (e.g., "if Q1 bookings fall below $Y, activate cost-reduction playbook")
- Present a decision table: scenario → metric outcome → recommended action → trigger/timeline
7. **Document and present**
- Summarize findings in an executive brief: key takeaways, scenario comparison table, sensitivity highlights, and recommended path
- Include an appendix with full assumption tables, model outputs, and methodology notes
- Clearly separate facts, assumptions, and recommendations throughout
## Output
- **Scenario summary table** — side-by-side comparison of 3–5 scenarios across all decision metrics
- **Sensitivity analysis exhibits** — tornado chart ranking variable impact; two-way tables for top pairs; breakeven thresholds
- **Decision matrix** — scenario-to-action mapping with triggers and timelines
- **Assumption register** — complete list of every variable, its value per scenario, source, and confidence level
- **Executive narrative** — 1–2 page summary suitable for board or leadership review
## Quality Checks
- Every assumption has a documented source; unsupported assumptions are tagged [VERIFY]
- Scenario definitions are mutually distinct and collectively span a realistic range — no two scenarios overlap excessively
- Model mechanics are validated: baseline scenario ties back to the approved budget or latest forecast within acceptable tolerance [VERIFY against current approved numbers]
- Sensitivity analysis covers all variables identified as high-impact; no material driver is omitted
- Decision triggers are specific and measurable, not vague ("monitor closely")
- Outputs are stress-tested for internal consistency — e.g., cash flow aligns with P&L and balance sheet movements
- Tax rates, depreciation schedules, and working capital assumptions are jurisdiction-appropriate [VERIFY]
- Presentation distinguishes clearly between deterministic outputs and probability-weighted expected values