diagramming-code
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
Install / Use
npx skills add trailofbits/skills --skill diagramming-codeInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Our assessment of diagramming-code
diagramming-code scores 95/100 on our quality scale, 284th of 3,044 Development & Engineering skills we index (top 10%).
Its SKILL.md is 6.6 KB long, well organised into 21 sections with 10 code examples: a thorough specification that gives an agent plenty to work with.
With 7,225 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 4 days ago, so diagramming-code is actively maintained.
- It is released under the CC-BY-SA-4.0 license; check its terms before commercial use.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-28. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
diagramming-code compared with similar skills
All 4 of these similar skills score higher than diagramming-code; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| diagramming-code (this skill)by trailofbits | 95 | 7.2k | 4d ago | SKILL.md |
| ai-job-searchby MadsLorentzen | 100 | 44.2k | today | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 1d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 5d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 5d ago | SKILL.md |
Frequently asked questions
- How do I install diagramming-code?
- Run
npx skills add trailofbits/skills --skill diagramming-code. The install tabs above show the steps for each supported agent. - Which AI agents does diagramming-code work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is diagramming-code safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is CC-BY-SA-4.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is diagramming-code still maintained?
- The repository was last updated 4 days ago, so diagramming-code is actively maintained.
Skill content
View source on GitHubname: 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
trailmarkskill) - Mutation testing triage (use the
genotoxicskill) - Architecture diagrams not derived from code (draw by hand)
Prerequisites
trailmark must be installed. If uv run trailmark fails, run:
uv tool install trailmark
# Python snippets: uv run --with trailmark python - (a tool env is not importable)
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
# Trailmark 0.4.0+ equivalent after the Version Gate succeeds
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
# 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
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
