Skills code-communities

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/athola/nm-cartograph-code-communities" ~/.claude/skills/openclaw-skills-code-communities && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/athola/nm-cartograph-code-communities" ~/.openclaw/skills/openclaw-skills-code-communities && rm -rf "$T"
manifest: skills/athola/nm-cartograph-code-communities/SKILL.md
source content

Night Market Skill — ported from claude-night-market/cartograph. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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