Stats
Actions
Tags
From skills-for-humanity
Assigns explicit probabilities to distinct scenarios before making a decision. Useful for quantifying uncertainty, scenario weighting, and probability distribution analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/skills-for-humanity:s4h-probability-scenario-weightingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Vague uncertainty — "it might work, it might not" — produces poor decisions. Quantified uncertainty forces precision about what is actually believed and makes implicit assumptions visible. Assigning explicit probabilities to scenarios is not prediction; it is structured belief articulation. The process of assigning and calibrating probabilities often reveals more than the final numbers.
Vague uncertainty — "it might work, it might not" — produces poor decisions. Quantified uncertainty forces precision about what is actually believed and makes implicit assumptions visible. Assigning explicit probabilities to scenarios is not prediction; it is structured belief articulation. The process of assigning and calibrating probabilities often reveals more than the final numbers.
Step 1: List Scenarios Enumerate all scenarios — they must be mutually exclusive (no overlap) and collectively exhaustive (cover all meaningful possibilities). If "other" is significant, make it explicit. Typically 3–5 scenarios; more than 6 reduces usability.
Framing check: Confirm the specific situation and uncertainty before continuing. State what you've identified — the actual decision or situation at stake and the key uncertainty being quantified — in one sentence, then use AskUserQuestion:
Step 2: Assign Initial Probabilities Assign a probability to each scenario. They must sum to 100%. Do this before analyzing each scenario in detail — your prior is informative and anchoring matters.
Step 3: Calibration Check For each probability: would you accept a bet at these odds? If you assigned 70% to a scenario, you should be willing to accept 3:7 odds against it. If the bet feels uncomfortable at those odds, your stated probability is wrong. Adjust.
Step 4: Identify Key Driver for Each Scenario What would need to be true for each scenario to occur? What is the single most important condition that must hold?
Step 5: Find the High-Probability and High-Impact Scenarios
Before narrowing: Show the complete calibrated scenario table to the user first. Use AskUserQuestion:
These may be the same scenario or different ones. If the highest-probability scenario is low-impact and the highest-impact scenario is low-probability, name both — they require different responses.
Step 6: Identify Most Useful Information What new information would most update these probabilities? This determines what to investigate next. Information that confirms the most likely scenario is usually less valuable than information that discriminates between scenarios.
Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:
Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.
Scenario Table
| Scenario | Probability | Key Driver (must be true) | Primary Implication |
|---|---|---|---|
| Total | 100% |
Highest-Probability Scenario: [name + %, implications]
Highest-Impact Scenario: [name + %, implications — note if same or different from above]
Most Useful Information to Gather: [the question whose answer would most shift these probabilities]
Resist collapsing scenarios into "optimistic / realistic / pessimistic" — this framing anchors on the most optimistic outcome and treats the middle case as truth. Build scenarios around key uncertainties, not emotional valence.
After delivering this output, use AskUserQuestion to offer the next move:
/s4h-decision-premortem-analysis — Run a premortem on the worst weighted scenarios/s4h-decision-criteria-weighting — Weight decision criteria against scenario probabilities/s4h-temporal-horizon-mapping — Map the weighted scenarios across time horizonsnpx claudepluginhub human-avatar/skills-for-humanityRoutes probabilistic thinking to the right skill: base-rate anchoring, confidence calibration, expected value, or scenario weighting. Activates on queries about probability, likelihood, and uncertainty.
Runs structured multi-branch scenario analysis for speculative questions, decision forks, and risk assessment. Maps possibility space with probability-weighted branches.
Builds 2–4 possible futures to stress-test strategic decisions against uncertainty. Use for long-range planning or when a single forecast is unreliable.