Call Chain
Trace how a function or entry point fans out through your codebase with scored paths and a Mermaid flow chart before refactoring or debugging.
Install
npx skills add https://github.com/athola/claude-night-market --skill call-chainWhat is this skill?
- Runs gauntlet graph_query.py flows with configurable depth and optional entry-point filter
- Scores criticality on traced paths for prioritizing review or incident triage
- Outputs Mermaid diagrams from the code knowledge graph
- Degrades to rg/grep static call tracing when gauntlet is missing
- Documents prerequisite: build graph with /gauntlet-graph build when graph.db is absent
Adoption & trust: 1 installs on skills.sh; 304 GitHub stars; 2/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
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Journey fit
Primary fit
Canonical shelf on Build because tracing execution through the graph is primarily done while implementing or navigating backend and service code. Backend subphase fits call-graph and flow tracing across modules, imports, and service boundaries rather than UI-only work.
Common Questions / FAQ
Is Call Chain safe to install?
skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.
SKILL.md
READMESKILL.md - Call Chain
# Call Chain Tracing Trace execution flows through the codebase using the code knowledge graph. ## Prerequisites This skill requires the **gauntlet** plugin for graph data. Discover it: ```bash GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1) ``` **If gauntlet is not installed**: Fall back to static analysis. Use `grep` to trace function calls and build a Mermaid diagram manually from import/call patterns. Skip graph-specific steps. **If installed but no graph.db**: Tell the user to run `/gauntlet-graph build`. ## Steps 1. **Accept target**: Get a function name or entry point from the user (or trace all entry points). 2. **Run flow tracing** (requires gauntlet): ```bash python3 "$GRAPH_QUERY" --action flows --depth 15 ``` To filter by entry point: ```bash python3 "$GRAPH_QUERY" --action flows --entry "main" ``` **Fallback (no gauntlet)**: Trace calls with rg (or grep): ```bash # 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**: ```mermaid 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 ## 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 |