---
name: yogsoth-ai/variance-decomposition
source: https://app.decimal.ai/s/yogsoth-ai-variance-decomposition@1/SKILL.md
source_sha256: 23e615f2c6bf
---

# Variance Decomposition

Quantify each parameter's contribution to output variance.

## Budget

| Base SOP | Target | ±10% Range |
|----------|--------|------------|
| web-search | 20 | 18–22 |
| web-research | 10 | 9–11 |
| paper-overview | 30 | 27–33 |
| paper-search | 25 | 22–28 |
| paper-research | 15 | 13–17 |

## State Ledger

```
<HARD-GATE>
| SOP | Done | Target | % |
|-----|------|--------|---|
| web-search | ? | 20 | ? |
| web-research | ? | 10 | ? |
| paper-overview | ? | 30 | ? |
| paper-search | ? | 25 | ? |
| paper-research | ? | 15 | ? |
Budget Gate: OPEN/CLOSED (>=80% required to exit)
</HARD-GATE>
```

## Available Tactics

- screening-then-decomposition

## Available SOPs

**Import:** web-search, web-research, paper-overview, paper-search, paper-research
**Subagent:** sobol-decomposition, interaction-detection

## Execution Guidance

For parameters surviving screening, compute Sobol first-order (Si) and total-order (STi) indices. Si measures direct effect, STi-Si measures interaction contribution. Focus on parameters with high STi.

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## Available Tactics

Optional, no fixed order; the final leaf is always a sop.

| Tactic | When to use |
| --- | --- |
| screening-then-decomposition | Two-phase sensitivity — Morris quick screening to eliminate unimportant factors, then Sobol precise decomposition on survivors. Efficient allocation of analytical effort. |

## Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use |
| --- | --- |
| interaction-detection | Detect and characterize significant parameter interactions from Sobol decomposition results. |
| sobol-decomposition | Sobol variance decomposition — compute first-order and total-order sensitivity indices for precise variance attribution. |

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