القائمة
Scenario planning toolkit — 2x2 matrices, four-case canvas, Monte Carlo setup, sensitivity & tornado, decision-tree EV, war-game, reverse stress test, optionality scoring.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | scenario-planning |
| description | Scenario planning toolkit — 2x2 matrices, four-case canvas, Monte Carlo setup, sensitivity & tornado, decision-tree EV, war-game, reverse stress test, optionality scoring. |
| allowed-tools | ["Read","Write","Glob","Grep"] |
Used by cso, chief-risk-officer, cfo, crisis-warroom, capital-allocation. Provides the analytic toolkit for thinking under uncertainty beyond single-point forecasts.
For any material decision:
| Case | Probability | Description | NPV / outcome | Key drivers |
|---|---|---|---|---|
| Base | (typically 50–60%) | Most-likely path | ||
| Upside | (typically 15–25%) | Drivers break favorably | ||
| Downside | (typically 15–25%) | Drivers break unfavorably | ||
| Black-swan | (typically 1–5%) | Tail event; structural break |
Recommendation lens: would we take this action even in the downside case (or only if upside)? What's our regret if black-swan hits?
| Step | What to do |
|---|---|
| 1. Identify drivers | 5–15 key uncertain inputs (revenue, margin, cost, tax, working capital) |
| 2. Choose distribution per driver | Normal (symmetric), lognormal (asymmetric, multiplicative), triangular (bounded), beta (constrained), empirical (fit history) |
| 3. Specify correlations | Revenue × margin is rarely independent. Use covariance matrix |
| 4. Run iterations | 10k+ for stable tails; 50k+ for P1 / P99 |
| 5. Report distribution | P5 / P25 / P50 / P75 / P95 — not just mean |
| 6. Tail decomposition | Which driver(s) dominate the bad tail? |
For sequential decisions with uncertainty:
Decision node [□]
├── Action A
│ Chance node (○)
│ ├── Outcome A1 (prob × value)
│ └── Outcome A2 (prob × value)
│ EV(A) = Σ p · v
└── Action B
...
Choose the action with highest EV — but report variance / downside too.
For strategy / competitive decisions:
Standard approach: scenario → outcome. Reverse: outcome → scenario.
The research doc highlights reverse stress as critical for black-swan preparedness.
For any bet, compute:
Total value = Static NPV + Option value − Reversibility cost
| Option type | Question | Approximate value |
|---|---|---|
| Defer | Can we wait for more information? | Black-Scholes-style on volatility |
| Expand | Can we scale up if it works? | Lattice on success probability |
| Abandon | Can we exit if it doesn't? | Salvage value relative to commitment |
| Stage | Can we phase commitment? | Multi-period lattice |
| Switch | Can we change use? | Spread option |
| Compound | Does this unlock further options? | Option-on-option |
Bets with high option value justify lower static NPV, especially under high uncertainty.
cso leads)Scenario artifacts saved per the requesting role (typically output/strategy/, output/finance/, output/risk/).
AI governance — EU AI Act Article 9, NIST AI RMF, ISO/IEC 42001 — risk classification, lifecycle gates, model cards, HITL policy, AI incident response.
Crisis response toolkit — classification, trigger thresholds, escalation tiers, war-room activation, rapid liquidity stress, supply-chain alternate-source, holding-statement, AAR.
Adversarial red-team debate protocol — when to use it, the 4-step structure, role-pair recipes, Referee scoring, termination conditions, MAS failure mitigations.
Enterprise risk management — COSO ERM 2017 + ISO 31000 — risk taxonomy, appetite statement, top-risk register, KRIs, Bow-Tie, three-lines-of-defense.
Enterprise executive decision frameworks, memo templates, board protocol, RACI, OKR, escalation rules, dissenting-opinion format.
Deterministic financial tools — WACC, NPV, IRR, payback, EVA, real-options, Monte Carlo, sensitivity, covenant/liquidity checks. The Financial Hardcoding Directive.