Agent skill · research science · sundial-org
decision-trees
Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.
Why this skill is useful
Provides structured decision-making frameworks and risk assessment techniques that the AI wouldn't reliably generate on its own.
What it needs
About 6k tokens when loaded. Last updated 2026-03-07. 639 stars on the source repository.
What this skill does
Decision Trees — Structured Decision-Making Decision tree analysis: a visual tool for making decisions with probabilities and expected value. When to Use ✅ Good for: Business decisions (investments, hiring, product launches) Personal choices (career, relocation, purchases) Trading & investing (position sizing, entry/exit) Operational decisions (expansion, outsourcing) Any situation with measurable consequences ❌ Not suitable for: Decisions with true uncertainty (black swans) Fast tactical choices Purely emotional/ethical questions Method Decision tree = tree-like structure where: Decision nodes (squares) — your actions Chance nodes (circles) — random events End nodes (triangles) — final outcomes Process: 1. Define options — all possible actions 2. Define outcomes — what can happen after each action 3. Estimate probabilities — how likely is each outcome (0-100%) 4. Estimate values — utility/reward for each outcome (money, points, utility units) 5. Calculate EV — expected value = Σ (probability × value) 6. Choose — option with highest EV Formula Example: Outcome A: 70% probability, +$100 → 0.7 × 100 = $70 Outcome B: 30% probability, -$50 → 0.3 × (-50) = -$15 EV = $70 + (-$15) = $55 Classic Example (from Wikipedia) Decision: Go to party or stay home? Estimates: Party: +9 utility (fun) Home: +3 utility (comfort) Carrying jacket unnecessarily: -2 utility Being cold: -10 utility Probability cold: 70% Probability warm: 30% Tree: Conclusion: Go and take jacket (EV = 8.4) > stay home (EV = 3.0) > go without jacket (EV = 2.0) Business Example Decision: Launch new product? Estimates: Success probability: 40% Failure probability: 60% Profit if success: $500K Loss if failure: $200K Don't launch: $0 Tree: Conclusion: Launch (EV = +$80K) is better than not launching ($0). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sundial-org/decision-trees