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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 cartographHow this skill is triggered — by the user, by Claude, or both
Slash command
/cartograph:call-chainThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Trace execution flows through the codebase using the
Traces code execution paths, data flows, and call graphs via /sourceatlas:flow queries. Reveals boundaries, entry points, cycles for understanding features and implementations.
Traces execution paths, maps dependencies, follows data flows, and explores unfamiliar code systematically from entry points to build incremental understanding.
Generates Mermaid diagrams from Trailmark code graphs: call graphs, class hierarchies, module dependencies, containment structures, complexity heatmaps, data flows. Use for visualizing code architecture and attack surfaces.
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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 |