Systematically quantify uncertain choices, make defensible decisions using expected value analysis, and communicate recommendations through persuasive narratives. This meta-skill chains estimation → decision → storytelling to transform ambiguous options into clear, stakeholder-ready recommendations.

Trigger phrases: "should we", "evaluate options", "make a case for", "ROI analysis", "expected value", "justify decision", "quantify tradeoffs", "pitch to", "business case", "cost-benefit", "probability-weighted"

A three-phase meta-skill that combines:

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Build custom: $200k-$400k dev cost (60% likely $300k), $50k/year maintenance Buy SaaS: $120k/year subscription, $20k implementation

Expected 3-year cost:

Expected value with risk adjustment:

"We evaluated building custom analytics vs. buying a SaaS solution. While building seems cheaper initially ($300k vs. $380k over 3 years), custom development carries significant risk—30% of similar projects experience 2x cost overruns, bringing expected cost to $510k. The SaaS solution offers predictable pricing, faster time-to-value (2 months vs. 8 months), and proven reliability. Recommendation: Buy the SaaS tool, saving $130k in expected costs and delivering value 6 months earlier."

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Copy this checklist and track your progress:

Chain Estimation → Decision → Storytelling Progress:
- [ ] Step 1: Clarify decision and gather inputs
- [ ] Step 2: Estimate uncertain variables
- [ ] Step 3: Analyze decision with expected value
- [ ] Step 4: Craft persuasive narrative
- [ ] Step 5: Validate and deliver

Define the choice (what decision needs to be made?), identify alternatives (2-5 options to compare), list uncertainties (what variables are unknown or probabilistic?), determine audience (who needs to be convinced?), and clarify constraints (budget, timeline, requirements). Ensure the decision is actionable and the options are mutually exclusive.

For each alternative, quantify costs (fixed, variable, opportunity), estimate benefits (revenue, savings, productivity), assign probabilities to scenarios (best case, base case, worst case), and perform sensitivity analysis (which inputs matter most?). Use ranges rather than point estimates. For simple cases → Use for structured estimation. For complex cases → Study for advanced techniques (Monte Carlo, decision trees, real options).

Calculate expected outcomes for each alternative (probability-weighted averages), compare using decision criteria (NPV, payback period, IRR, utility), identify dominant option (best expected value or risk-adjusted return), and test robustness (does conclusion hold across reasonable input ranges?). Document assumptions explicitly. See Common Patterns for decision-type specific approaches.

Structure story with: problem statement (why this decision matters), alternatives considered (show you did the work), analysis summary (key numbers and logic), recommendation (clear choice with reasoning), next steps (what happens if approved). Tailor to audience: executives want bottom line and risks, technical teams want methodology and assumptions, finance wants numbers and sensitivity.

Self-check using . Verify: estimates are justified with sources/logic, probabilities are calibrated (not overconfident), expected value calculation is correct, sensitivity analysis identifies key drivers, narrative is clear and persuasive, assumptions are stated explicitly, risks and limitations are acknowledged. Minimum standard: Score ≥ 3.5. Create output file with full analysis and recommendation.