From cartograph
Traces execution paths from entry points through code knowledge graph. Shows call chains with criticality scores and generates Mermaid flowcharts for flow analysis.
npx claudepluginhub athola/claude-night-market --plugin cartographThis skill uses the workspace's default tool permissions.
Trace execution flows through the codebase using the
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Trace execution flows through the codebase using the code knowledge graph.
This skill requires the gauntlet plugin for graph data. Discover it:
GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed: Fall back to static
analysis. Use grep to trace function calls and build
a Mermaid diagram manually from import/call patterns.
Skip graph-specific steps.
If installed but no graph.db: Tell the user to run
/gauntlet-graph build.
Accept target: Get a function name or entry point from the user (or trace all entry points).
Run flow tracing (requires gauntlet):
python3 "$GRAPH_QUERY" --action flows --depth 15
To filter by entry point:
python3 "$GRAPH_QUERY" --action flows --entry "main"
Fallback (no gauntlet): Trace calls with rg (or grep):
# Prefer rg (ripgrep) for speed; fall back to grep
if command -v rg &>/dev/null; then
rg -n "function_name\(" --type py . | head -20
else
grep -rn "function_name(" --include="*.py" . | head -20
fi
Build the call tree manually from search results.
Display as indented tree:
main() [criticality: 0.72]
-> validate_input()
-> parse_config()
-> process_data()
-> db.execute_query()
-> cache.store()
-> send_response()
Generate Mermaid flowchart:
flowchart LR
main --> validate_input
main --> process_data
main --> send_response
validate_input --> parse_config
process_data --> db.execute_query
process_data --> cache.store
Show criticality breakdown:
| Factor | Weight | Meaning |
|---|---|---|
| File spread | 0.30 | Touches many files |
| Security | 0.25 | Contains auth/crypto code |
| External calls | 0.20 | Unresolved dependencies |
| Test gap | 0.15 | Untested nodes in flow |
| Depth | 0.10 | Deep call chains |