uncertainty-propagation

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/uncertainty-propagation

Propagate input uncertainties through models using Monte Carlo sampling

  • Quantifies how input uncertainty affects model output variability
  • Uses web and paper research to assign probability distributions
  • Identifies critical inputs via statistical analysis of Monte Carlo results
  • Delivers output uncertainty metrics and sensitivity rankings

SKILL.md

.github/skills/uncertainty-propagationView on GitHub ↗
---
name: uncertainty-propagation
description: Propagate input uncertainties through the model via Monte Carlo sampling. Identifies which input uncertainties contribute most to output uncertainty.
used-by: sensitivity-analysis
---

# Uncertainty Propagation

Map how input uncertainty flows to output uncertainty.

## Budget

| Base SOP | Target | ±10% Range |
|----------|--------|------------|
| web-search | 20 | 18–22 |
| web-research | 10 | 9–11 |
| paper-overview | 25 | 22–28 |
| paper-search | 20 | 18–22 |
| paper-research | 10 | 9–11 |

## State Ledger

```
<HARD-GATE>
| SOP | Done | Target | % |
|-----|------|--------|---|
| web-search | ? | 20 | ? |
| web-research | ? | 10 | ? |
| paper-overview | ? | 25 | ? |
| paper-search | ? | 20 | ? |
| paper-research | ? | 10 | ? |
Budget Gate: OPEN/CLOSED (>=80% required to exit)
</HARD-GATE>
```

## Available Tactics

- uncertainty-cascade

## Available SOPs

**Import:** web-search, web-research, paper-overview, paper-search, paper-research
**Subagent:** distribution-assignment, monte-carlo-sampling, critical-path-identification

## Execution Guidance

Assign probability distributions to uncertain inputs, propagate through the model via Monte Carlo sampling, analyze output distribution, identify which input uncertainties contribute most.

More from yogsoth-ai/de-anthropocentric-research-engine

SkillDescription
abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
ablation-component-mappingMap system architecture to ablatable units for ablation studies
ablation-designDesign ablation studies to isolate component contributions in ML systems
ablation-executionRemove components one by one from a system, record the response/impact of each removal.
abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
abstraction-ladderPerform bisociation at multiple abstraction levels
abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.