{"id":18427,"plugin_id":"plugins_6a893288a1008191857f5437d78ab047","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:14:48.914Z","digest":"5bc57cb7ddaacc24f1e0c11a0bc94d666ced8eb7bf7f21ec3520572c4b1f0d29","against":null,"payload":{"description":"Use for campaign what-if questions, budget or KPI forecasts, sensitivity analysis, and decision ranges where deterministic or probabilistic scenarios are more useful than a single point estimate.","included_files":[{"relative_path":"agents/openai.yaml","size_in_bytes":285}],"name":"scenario-simulation","skill_md_contents":"---\nname: scenario-simulation\ndescription: Use for campaign what-if questions, budget or KPI forecasts, sensitivity analysis, and decision ranges where deterministic or probabilistic scenarios are more useful than a single point estimate.\n---\n\n# Scenario Simulation\n\nUse simulation to expose decision ranges and sensitivity. Do not decorate weak assumptions with false precision.\n\n## Choose the method\n\nUse the simplest defensible method:\n\n- deterministic scenario table for direct arithmetic changes\n- sensitivity analysis when one or more assumptions drive the result\n- bootstrap/resampling when representative historical observations are available\n- Monte Carlo only when probability distributions or defensible uncertainty ranges can be specified\n\nUse host-native Python when available for non-trivial calculations. Report the executed method and assumptions.\n\n## Define the model\n\nSpecify:\n\n- target metric and horizon\n- starting state\n- controllable inputs\n- uncertain inputs\n- constraints\n- relationship assumptions\n- number of simulations, if applicable\n\nDo not silently assume that CPA, ROAS, CVR, or CPM remains constant as spend changes. If a constant-rate scenario is useful as a baseline, label it explicitly.\n\n## Scenario set\n\nTypically compare:\n\n- base case\n- conservative case\n- expected/planning case\n- upside case\n\nFor budget decisions, include the current allocation as a control scenario.\n\n## Sensitivity\n\nIdentify which assumptions have the largest influence on the decision. If a small change in one uncertain parameter flips the recommendation, the answer should emphasize measurement rather than confidence.\n\n## Output contract\n\nReturn:\n\n- model and assumptions\n- scenario results or distributions\n- sensitivity drivers\n- decision boundary\n- what the model cannot infer\n- next measurement that would reduce uncertainty most\n\nA simulated result is not evidence that the future will occur. Keep observed data and modeled outcomes separate.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}