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code-communities

Detects architectural clusters and coupling boundaries via community detection on the code graph. Use when identifying module groupings or refactoring targets.

314estrelas
Atualizado há 4 meses

Ver no GitHub ↗ Licença: MIT

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Code Community Detection

Identify architectural clusters and module boundaries in the codebase.

Prerequisites

This skill requires the gauntlet plugin for graph data. Discover it:

GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)

If gauntlet is not installed: Fall back to directory structure analysis. Group files by directory and use import statements to identify module boundaries. Generate a Mermaid diagram from directory-level relationships.

If installed but no graph.db: Tell the user to run /gauntlet-graph build.

Steps

  1. Run community detection (requires gauntlet):

``bash python3 "$GRAPH_QUERY" --action communities ``

Fallback (no gauntlet): Analyze directory structure and cross-directory imports: ```bash # Directory-level grouping find . -name ".py" -not -path "/node_modules/" | \ sed 's|/[^/]$||' | sort | uniq -c | sort -rn

# Cross-directory imports (rg preferred, grep fallback) if command -v rg &>/dev/null; then rg "^from |^import " --type py -l . | \ xargs -I{} rg "^from \w+ import|^import \w+" {} --no-filename else grep -rh "^from \|^import " --include="*.py" . fi | sort | uniq -c | sort -rn | head -20 ``` Group by top-level directories and count cross-directory imports to estimate coupling.

  1. Display clusters:

`` Community | Nodes | Cohesion | Description auth | 12 | 0.85 | Authentication module db | 8 | 0.92 | Database access layer api/handlers | 15 | 0.71 | API request handlers utils | 6 | 0.45 | Shared utilities ``

  1. Show coupling warnings: If communities have

>10 cross-boundary edges, highlight them: `` WARNING: High coupling between 'auth' and 'api/handlers' (23 cross-community edges, severity: high) ``

  1. Generate Mermaid diagram:

``mermaid flowchart TB subgraph auth[Auth Module - cohesion 0.85] verify_token check_permissions end subgraph db[DB Layer - cohesion 0.92] execute_query connection_pool end auth -->|"23 edges"| api db -->|"5 edges"| api ``

  1. Suggest improvements:
  2. Low cohesion (<0.5): "Consider splitting this

module into more focused components"

  • High coupling (>20 edges): "Consider introducing

an interface to reduce direct dependencies"

Algorithm

Uses the Leiden algorithm (when igraph is available) with edge-type-specific weights. Falls back to file-based grouping otherwise.

| Edge Type | Weight | |-----------|--------| | CALLS | 1.0 | | INHERITS | 0.8 | | IMPLEMENTS | 0.7 | | IMPORTS_FROM | 0.5 | | TESTED_BY | 0.4 | | CONTAINS | 0.3 |

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