# Metric Decomposition

> Decompose composite metrics into constituent signals, analyze polarity and ceiling effects

- Skill: `yogsoth-ai/metric-decomposition` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/metric-decomposition`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/metric-decomposition/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/yogsoth-ai/metric-decomposition

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# Metric Decomposition SOP

Decompose a benchmark's evaluation metric into its constituent signals to understand what is actually being measured, identify ceiling effects, and detect polarity issues.

## Input

- **metric_name**: Name of the metric (e.g., "F1", "BLEU", "Pass@k", "ELO")
- **metric_definition**: Mathematical definition or description of how the metric is computed
- **score_distribution**: Known score distribution data (top scores, median, floor)

## Procedure

1. Parse metric formula into component signals
2. Identify what each component rewards/penalizes
3. Analyze ceiling and floor effects
4. Check for known pathologies (metric gaming, Goodhart's law instances)
5. Assess granularity and discriminative power at current SOTA level

## Output

Structured decomposition showing constituent signals, their weights, ceiling effects, and known pathologies.

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## Available SOPs

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

| SOP | When to use |
| --- | --- |
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |

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