Call Chain Tracing
SkillAI & modelsTraces execution paths through the code graph with criticality scoring and Mermaid charts. Use when understanding how a function propagates through the system.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Call Chain Tracing skill
What this skill tells your AI
The instructions your AI receives, as published by athola/claude-night-market in plugins/cartograph/skills/call-chain/SKILL.md and read by ahel’s review.
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=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed: Fall back to static
analysis. Use grep to trace function calls and build
a Mermaid diagram manually from import/call patterns.
Skip graph-specific steps.
If installed but no graph.db: Tell the user to run
/gauntlet-graph build.
Steps
-
Accept target: Get a function name or entry point from the user (or trace all entry points).
-
Run flow tracing (requires gauntlet):
python3 "$GRAPH_QUERY" --action flows --depth 15To 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 fiBuild 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:
- File spread: how many files the flow touches
- Security sensitivity: auth/crypto code in the path
- Test coverage gaps: untested nodes in the flow
Criticality Scoring
| 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 |
Exit Criteria
- Indented call tree displayed for the target function with
criticality scores in the form
[criticality: N.NN] - Mermaid
flowchart LRgenerated with edges representing each caller-to-callee relationship in the traced path - Criticality breakdown table shown covering: file spread, security sensitivity, external calls, test gap, and depth
- If gauntlet is not installed, fallback to static
rg/grepanalysis is used and the absence of graph data is noted - If gauntlet is installed but
graph.dbis absent, user is told to run/gauntlet-graph buildbefore the skill halts
Signals
- GitHub stars
- 337
- Forks
- 34
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
call-chain- Source
- github.com/athola/claude-night-market