decision-making
Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive process.
npx skills add majiayu000/claude-skill-registry --skill decision-making --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Decision Making A decision is a commitment to one of several possible actions in the face of uncertainty about their consequences. Good decision-making is not the same as getting good outcomes — luck intervenes — but consistently good decisions produce better outcomes over time. This skill covers the structured methods decision scientists use to bring rigor to choices that matter. **Agent affinity:** kahneman-ct (System 1 / System 2 allocation), tversky (expected value and biases), paul (integration with elements of reasoning) **Concept IDs:** crit-decision-frameworks, crit-calibrated-confidence, crit-intellectual-humility ## The Decision Toolbox at a Glance | # | Method | Purpose | When to use | |---|---|---|---| | 1 | Expected value calculation | Weigh probabilities and payoffs | Repeatable decisions with quantifiable outcomes | | 2 | Decision trees | Map sequential choices and chance nodes | Multi-stage decisions with contingencies | | 3 | Multi-criteria decision analysis (MCDA) | Weigh multiple incommensurable criteria | Choices involving trade-offs across dimensions | | 4 | Pros and cons with weights | Simple MCDA for everyday decisions | Personal choices, not enough data fo
- The Decision Toolbox at a Glance
- The Fundamental Distinction — Good Decision vs. Good Outcome
- Method 1 — Expected Value
- Method 2 — Decision Trees
- Method 3 — Multi-Criteria Decision Analysis
- Method 4 — Pre-Mortem
- Method 5 — Reversibility and the Two-Way Door
- Method 6 — Minimax Regret
- Method 7 — Satisficing
- Method 8 — System 1 vs. System 2 Allocation
- Method 9 — Pros and Cons with Weights
- Method 10 — Stopping Rules
- Standard Decision Procedure
- When to Use
What does the decision-making skill do?
Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive process.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill decision-making --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.