Cluster Analysis
SkillDev toolsIdentify natural opinion clusters from collected judgments and characterize
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Cluster Analysis skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/cluster-analysis/SKILL.md and read by ahel’s review.
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.
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. |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
cluster-analysis- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine