maxidonkey/delphigenai/delphi-uses-graph

Claude Code and agent skill by maxidonkey/delphigenai

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Checking that the SKILL.md file is still in the repository.

Analyzes a Delphi / Object Pascal codebase to extract unit-level `uses` dependencies. Use when the user uploads a zip / archive of a Delphi project, or a folder of .pas / .dpr / .dpk files, and asks for a dependency graph, architecture map, cycle detection, fan-in / fan-out coupling analysis, or wants to know how units relate. Produces a Mermaid diagram, Graphviz DOT, optional SVG, JSON dependency dump, and markdown report.

Install

npx vskill@latest install maxidonkey/delphigenai/delphi-uses-graph

SKILL.md

Delphi uses-graph skill

This skill builds a directed graph of uses dependencies between Delphi / Object Pascal units, computes coupling metrics, and detects circular dependencies.

OpenAI Responses container note

This OpenAI-adapted bundle is intended to be registered through the OpenAI Skills API and attached to a Responses shell/container tool as a skill_reference. The analysis logic is vendor-neutral; the OpenAI-specific requirement is that the container can run Python and can read the uploaded Delphi project files.

When to use this skill

  • The user uploads an archive (.zip, .tar, .tar.gz) containing a Delphi

project, or a folder of .pas / .dpr / .dpk sources, and asks for an architecture map or a dependency graph.

  • The user asks "which unit depends on what?", "are there cycles?",

"what's the fan-in / fan-out?" against a Delphi codebase.

  • The user wants a visual artifact (Mermaid, SVG, DOT) summarizing unit-level

coupling.

Do not use this skill for non-Pascal codebases.

Inputs

The skill expects exactly one of:

  1. A .zip / .tar / .tar.gz archive of a Delphi project.
  2. A directory already containing .pas, .dpr, or .dpk files.

If the user pastes raw Pascal source instead, persist it to one or more temporary .pas files first, then point --input at their parent directory.

Workflow

  1. Locate the input archive or directory in the working area.
  2. Run scripts/tool.py with --input pointing at it and --output pointing

at a fresh output folder.

  1. Read the generated report.md and summarize parsed units, fan-in/fan-out,

cycles, and orphan units.

  1. Attach or surface the generated artifacts: Mermaid, DOT, SVG when present,

JSON, and report. Embed uses-graph.mmd inline when the conversation surface supports Mermaid rendering.

Quick start

bash
python scripts/tool.py \
--input /path/to/project.zip \
--output /path/to/out

If the container exposes python3 instead of python, use python3 with the same arguments.

Useful flags, with full details in reference.md:

  • --scope {all,interface,implementation} - which uses sections to

consider. Default all.

  • --ignore-prefix LIST - drop edges whose target starts with one of these

comma-separated prefixes, case-insensitive. Default: System,Winapi,Vcl,FMX,Data,Web,REST,IdGlobal. Pass "" to keep RTL/VCL.

  • --max-label N - truncate node labels in the Mermaid output. Default 40.
  • --include-orphans - keep units that have no inbound and no outbound edges

in the graph.

Outputs

Files written in --output:

FilePurpose
dependencies.jsonRaw graph: { unit: { defined_in, interface_uses, implementation_uses } }
uses-graph.mmdMermaid graph LR ready to embed in markdown
uses-graph.dotGraphviz DOT source
uses-graph.svgSVG rendering, only when the dot binary is available on PATH
report.mdHuman-readable summary: counts, hotspots, cycles, orphans

Notes

  • RTL / VCL / FMX targets are filtered by default to keep the diagram readable.

The user's own units are never filtered.

  • Cycle detection uses Tarjan's strongly connected components on the directed

graph. Edge A -> B means A uses B. Self-loops are reported separately.

  • Pascal uses syntax has several edge cases: dotted names, in '<path>'

aliases, conditional defines, and three comment styles. Read reference.md before changing the parser.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
87
Repository forks
20
7-day trend score
22

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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