call-chain-x-2

原始内容

---
name: call-chain
description: |
  Traces execution paths through the code graph with criticality scoring and Mermaid charts
version: 1.9.8
triggers:
  - understanding how a function propagates through the system
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/cartograph", "emoji": "\ud83e\udd9e"}}
source: claude-night-market
source_plugin: cartograph
---

> **Night Market Skill** — ported from [claude-night-market/cartograph](https://github.com/athola/claude-night-market/tree/master/plugins/cartograph). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


# 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 |

name: call-chain description: | Traces execution paths through the code graph with criticality scoring and Mermaid charts version: 1.9.8 triggers:

understanding how a function propagates through the system metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/cartograph", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: cartograph

Night Market Skill — ported from claude-night-market/cartograph. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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