# Cluster Analysis

> Identify natural opinion clusters from collected judgments and characterize each cluster.

- Skill: `yogsoth-ai/cluster-analysis` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/cluster-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/cluster-analysis/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/cluster-analysis

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# Cluster Analysis

Identify natural groupings of similar positions within the collected judgments. Characterize each cluster by its central position, shared reasoning patterns, and distinguishing features.

## Execution

Spawn a subagent that analyzes the judgments for similarity patterns, groups them into coherent clusters, and provides characterization of each cluster.

## Why Subagent

- Clustering requires holistic analysis of all judgments simultaneously
- Characterization is a bounded analytical task
- Output structure is standardized

## HARD-GATE

Output MUST contain: at least 2 clusters (if genuine disagreement exists), each with `cluster_id`, `position_summary`, `member_count`, and `characterization`. If all judgments agree, output 1 cluster with a note.

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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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