Key Findings

Discovered Topics

📝
User Interface
Code style rules, event handling, and error patterns
change
0.5099
task
0.405
git
0.3748
commit
0.3356
user
0.3292
rule
0.3026
phase
0.2905
dont
0.2864
new
0.2803
work
0.28

This topic represents a cluster of related terms discovered through topic modeling analysis. The word weights indicate how strongly each term is associated with this theme across the analyzed repositories.

📊
Agentsmd & Read
Cross-cutting concerns: validation, APIs, and error handling
agentsmd
2.1054
read
0.0616
require
0.0552
rule
0.0458
para
0.0449
guidance
0.0428
convention
0.0388
documentation
0.0383
primary
0.0364
detailed
0.0354

This topic represents a cluster of related terms discovered through topic modeling analysis. The word weights indicate how strongly each term is associated with this theme across the analyzed repositories.

⚛️
Frontend Development
React, TypeScript, npm tooling, and client-side patterns
npm
0.6847
component
0.5271
pnpm
0.3776
react
0.3123
typescript
0.3055
api
0.3015
page
0.2967
server
0.2798
cs
0.2534
dev
0.2263

Frontend-focused CLAUDE.md files are among the most detailed. The prevalence of "npm," "typescript," and "react" reflects the dominance of the React/TypeScript ecosystem in projects using Claude Code. These files typically specify component patterns, state management, and build tooling.

🔧
Data & APIs
API design, databases, server config, and integrations
python
0.4861
model
0.3532
bash
0.2614
api
0.2487
pytest
0.2367
docker
0.2313
data
0.226
backend
0.2089
service
0.2073
module
0.1988

This topic represents a cluster of related terms discovered through topic modeling analysis. The word weights indicate how strongly each term is associated with this theme across the analyzed repositories.

Agent & Skill
Documentation structure, user context, and task workflows
agent
0.9394
skill
0.8412
tool
0.2904
instruction
0.1843
session
0.1816
task
0.1652
mcp
0.1637
issue
0.1538
context
0.1518
work
0.1301

This topic represents a cluster of related terms discovered through topic modeling analysis. The word weights indicate how strongly each term is associated with this theme across the analyzed repositories.