uncertainty-cascade
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/uncertainty-cascadeMaps how input uncertainty propagates to output uncertainty using Monte Carlo sampling
- Quantifies how input variability affects model outputs
- Uses distribution-assignment, monte-carlo-sampling, and critical-path-identification subagents
- Prioritizes inputs based on their contribution to output variance
- Produces ranked critical paths and output distribution characteristics
SKILL.md
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--- name: uncertainty-cascade description: Uncertainty cascade propagation — assign input distributions, sample via Monte Carlo, propagate through model, analyze output distribution, identify critical paths. Maps how input uncertainty flows to output uncertainty. execution: tactic used-by: uncertainty-propagation --- # Uncertainty Cascade Map how input uncertainty flows through to output uncertainty. ## Operations distribution-assignment → monte-carlo-sampling → critical-path-identification ## Available SOPs **Subagent:** distribution-assignment, monte-carlo-sampling, critical-path-identification **Import:** paper-search ## Execution Guidance Assign distributions to all uncertain inputs (use literature for informed priors), sample (Latin Hypercube for efficiency), propagate, analyze output distribution shape and spread, identify which inputs contribute most to output variance (critical path). Focus on: which inputs, if resolved, would most reduce output uncertainty? This directly informs research prioritization. ## Minimum Yield ``` <HARD-GATE> - Input distributions assigned: >= 4 - Propagation completed: yes - Output distribution characterized: yes - Critical path identified: >= 1 path with ranked inputs </HARD-GATE> ```