diagramming-code

name

diagramming-code

description

Generates Mermaid diagrams from Trailmark code graphs. Produces call graphs, class hierarchies, module dependency maps, containment diagrams, complexity heatmaps, and attack surface data flow visualizations. Use when visualizing code architecture, drawing call graphs, generating class diagrams, creating dependency maps, producing complexity heatmaps, or visualizing data flow and attack surface paths as Mermaid diagrams.

Diagramming Code

Generates Mermaid diagrams from Trailmark's code graph. A pre-made script handles Mermaid syntax generation; Claude selects the diagram type and parameters.

When to Use

• Visualizing call paths between functions
• Drawing class inheritance hierarchies
• Mapping module import dependencies
• Showing class structure with members
• Highlighting complexity hotspots with color coding
• Tracing data flow from entrypoints to sensitive functions

When NOT to Use

• Querying the graph without visualization (use the trailmark skill)
• Mutation testing triage (use the genotoxic skill)
• Architecture diagrams not derived from code (draw by hand)

Prerequisites

trailmark must be installed. If uv run trailmark fails, run:

uv pip install trailmark

DO NOT fall back to hand-writing Mermaid from source code reading. The script uses Trailmark's parsed graph for accuracy. If installation fails, report the error to the user.

---

Quick Start

uv run {baseDir}/scripts/diagram.py \
    --target {targetDir} --language auto --type call-graph \
    --focus main --depth 2

Output is raw Mermaid text. Wrap in a fenced code block:

```mermaid
flowchart TB
    ...
```

---

Diagram Types

├─ "Who calls what?"               → --type call-graph
├─ "Class inheritance?"             → --type class-hierarchy
├─ "Module dependencies?"           → --type module-deps
├─ "Class members and structure?"   → --type containment
├─ "Where is complexity highest?"   → --type complexity
└─ "Path from input to function?"   → --type data-flow

For detailed examples of each type, see references/diagram-types.md.

---

Workflow

Diagram Progress:
- [ ] Step 1: Verify trailmark is installed
- [ ] Step 2: Identify diagram type from user request
- [ ] Step 3: Determine focus node and parameters
- [ ] Step 4: Run diagram.py script
- [ ] Step 5: Verify output is non-empty and well-formed
- [ ] Step 6: Embed diagram in response

Step 1: Run uv run trailmark analyze --language auto --summary {targetDir}. Install if it fails. Then run pre-analysis via the programmatic API:

from trailmark.query.api import QueryEngine

engine = QueryEngine.from_directory("{targetDir}", language="auto")
engine.preanalysis()

Pre-analysis enriches the graph with blast radius, taint propagation, and privilege boundary data used by data-flow diagrams.

If auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as python,rust.

Step 2: Match the user's request to a --type using the decision tree above.

Step 3: For call-graph and data-flow, identify the focus function. Default --depth 2. Use --direction LR for dependency flows.

Step 4: Run the script and capture stdout.

Step 5: Check: output starts with flowchart or classDiagram, contains at least one node. If empty or malformed, consult references/mermaid-syntax.md.

Step 6: Wrap output in ```mermaid ``` code fence.

---

Script Reference

uv run {baseDir}/scripts/diagram.py [OPTIONS]

Argument	Short	Default	Description
--target	-t	required	Directory to analyze
--language	-l	python	Source language
--type	-T	required	Diagram type (see above)
--focus	-f	none	Center diagram on this node
--depth	-d	2	BFS traversal depth
--direction		TB	Layout: TB (top-bottom) or LR (left-right)
--threshold		10	Min complexity for complexity type

Examples

# Call graph centered on a function
uv run {baseDir}/scripts/diagram.py -t src/ -T call-graph -f parse_file

# Class hierarchy for a Rust project
uv run {baseDir}/scripts/diagram.py -t src/ -l rust -T class-hierarchy

# Module dependency map, left-to-right
uv run {baseDir}/scripts/diagram.py -t src/ -T module-deps --direction LR

# Class members
uv run {baseDir}/scripts/diagram.py -t src/ -T containment

# Complexity heatmap (threshold 5)
uv run {baseDir}/scripts/diagram.py -t src/ -T complexity --threshold 5

# Data flow from entrypoints to a specific function
uv run {baseDir}/scripts/diagram.py -t src/ -T data-flow -f execute_query

---

Customization

Direction: Use TB (default) for hierarchical views, LR for left-to-right flows like dependency chains.

Depth: Increase --depth to see more of the call graph. Decrease to reduce clutter. The script warns if the diagram exceeds 100 nodes.

Focus: Always use --focus for call-graph on non-trivial codebases. For data-flow, omitting focus auto-targets the top 10 complexity hotspots.

Language: Prefer --language auto for polyglot or unfamiliar repos. Use an explicit language only when you know the target is single-language or you need to exclude unrelated components.

---

Supporting Documentation

• references/diagram-types.md - Detailed docs and Mermaid examples for each diagram type
• references/mermaid-syntax.md - ID sanitization, escaping, style definitions, and common pitfalls