From cartograph
Identifies module groupings and coupling boundaries by running community detection on the code graph. Useful for refactoring and architectural analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cartograph:code-communitiesThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Identify architectural clusters and module boundaries
Identify architectural clusters and module boundaries in the codebase.
cartograph:dependency-graph)cartograph:architecture-diagram)This skill requires the gauntlet plugin for graph data. Discover it:
GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed: Fall back to directory structure analysis. Group files by directory and use import statements to identify module boundaries. Generate a Mermaid diagram from directory-level relationships.
If installed but no graph.db: Tell the user to run
/gauntlet-graph build.
Run community detection (requires gauntlet):
python3 "$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.
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
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)
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
Suggest improvements:
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 |
flowchart TB generated with one subgraph per community
showing cohesion score in the subgraph labelnpx claudepluginhub athola/claude-night-market --plugin cartographAnalyze Python module architecture using pyscn: class coupling (CBO), cohesion (LCOM), dependency cycles, and module community detection. Helpful for understanding module structure and planning refactors.
Maps unfamiliar codebases into feature/business domains using 47 peer-reviewed algorithms for graph construction, community detection, architecture recovery, topic modelling, and validation. Useful for onboarding, dependency mapping, feature location, and refactoring review.
Maps module boundaries, traces dependencies, and identifies coupling patterns across a codebase using the grepika MCP server.