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code-communities

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

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/code-communities/SKILL.md

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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):

    python3 "$GRAPH_QUERY" --action communities
    

    Fallback (no gauntlet): Analyze directory structure and cross-directory imports:

    # 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.

  2. 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
    
  3. 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)
    
  4. Generate Mermaid diagram:

    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
    
  5. Suggest improvements:

    • 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 TypeWeight
CALLS1.0
INHERITS0.8
IMPLEMENTS0.7
IMPORTS_FROM0.5
TESTED_BY0.4
CONTAINS0.3