用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/DojoGenesis/plugins --skill diagramming-code命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | diagramming-code |
| description | >. Trigger phrases: "invoke this skill". |
Generates Mermaid diagrams from Trailmark's code graph. A pre-made script handles Mermaid syntax generation; Claude selects the diagram type and parameters.
trailmark skill)genotoxic skill)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.
uv run {baseDir}/scripts/diagram.py \
--target {targetDir} --type call-graph \
--focus main --depth 2
Output is raw Mermaid text. Wrap in a fenced code block:
```mermaid
flowchart TB
...
```
├─ "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.
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 --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="{lang}")
engine.preanalysis()
Pre-analysis enriches the graph with blast radius, taint propagation,
and privilege boundary data used by data-flow diagrams.
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.
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 |
# 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
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.