call-chain

Call Chain Tracing

Trace execution flows through the codebase using the code knowledge graph.

When NOT To Use

• Static import relationships (use cartograph:dependency-graph)
• Scoring the risk of a change (use pensive:blast-radius)

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 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.

Steps

1. Accept target: Get a function name or entry point from the user (or trace all entry points).

2. 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.

3. Display as indented tree:

   main() [criticality: 0.72]
     -> validate_input()
       -> parse_config()
     -> process_data()
       -> db.execute_query()
       -> cache.store()
     -> send_response()
   

4. 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
   

5. Show criticality breakdown:

   • File spread: how many files the flow touches
   • Security sensitivity: auth/crypto code in the path
   • Test coverage gaps: untested nodes in the flow