Skill

call-chain

Traces execution paths from entry points through code knowledge graph. Shows call chains with criticality scores and generates Mermaid flowcharts for flow analysis.

Mermaid

Python

Bash

developer-tools

Install

npx claudepluginhub athola/claude-night-market --plugin cartograph

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Trace execution flows through the codebase using the

SKILL.md

Similar Skills

using-git-worktrees

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.

superpowers

158.9k

subagent-driven-development

3 files

Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.

superpowers

158.9k

dispatching-parallel-agents

Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.

superpowers

158.9k

Stats

Parent Repo Stars242

Parent Repo Forks24

Last CommitApr 4, 2026

Actions

View Source View Plugin View on GitHub View README

Tags

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:

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 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:

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

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:

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 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:
- 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