maxidonkey/delphigenai/delphi-uses-graph
Claude Code and agent skill by maxidonkey/delphigenai
- community
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-graphSKILL.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:
- A
.zip/.tar/.tar.gzarchive of a Delphi project. - A directory already containing
.pas,.dpr, or.dpkfiles.
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
- Locate the input archive or directory in the working area.
- Run
scripts/tool.pywith--inputpointing at it and--outputpointing
at a fresh output folder.
- Read the generated
report.mdand summarize parsed units, fan-in/fan-out,
cycles, and orphan units.
- 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
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}- whichusessections 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. Default40.--include-orphans- keep units that have no inbound and no outbound edges
in the graph.
Outputs
Files written in --output:
| File | Purpose | |
|---|---|---|
dependencies.json | Raw graph: { unit: { defined_in, interface_uses, implementation_uses } } | |
uses-graph.mmd | Mermaid graph LR ready to embed in markdown | |
uses-graph.dot | Graphviz DOT source | |
uses-graph.svg | SVG rendering, only when the dot binary is available on PATH | |
report.md | Human-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
usessyntax 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
- SKILL.md in the repositoryCheckingdemos/bin32/FMX_OpenAI/delphi-uses-graph/SKILL.md
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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