| 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. Trailmark 0.4.0 includes a native trailmark diagram command; use
it only after a version/command check, otherwise use this skill's bundled
script.
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.
Version Gate
Check whether native v0.4 diagram support exists:
trailmark diagram --help 2>/dev/null || uv run trailmark diagram --help 2>/dev/null
If this succeeds, you may use trailmark diagram. If it fails, use
uv run {baseDir}/scripts/diagram.py, which keeps the older skill workflow
intact. Do not assume the native CLI exists on Trailmark 0.2.x.
Quick Start
uv run {baseDir}/scripts/diagram.py \
--target {targetDir} --language auto --type call-graph \
--focus main --depth 2
uv run trailmark diagram \
--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 (or native trailmark diagram on v0.4+)
- [ ] 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.
If the native v0.4 CLI is available, either command is acceptable; prefer the
bundled script when you need behavior consistent with this skill's references.
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]
# or, on Trailmark 0.4.0+:
uv run trailmark diagram [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
uv run {baseDir}/scripts/diagram.py -t src/ -T call-graph -f parse_file
uv run {baseDir}/scripts/diagram.py -t src/ -l rust -T class-hierarchy
uv run {baseDir}/scripts/diagram.py -t src/ -T module-deps --direction LR
uv run {baseDir}/scripts/diagram.py -t src/ -T containment
uv run {baseDir}/scripts/diagram.py -t src/ -T complexity --threshold 5
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
