Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MaxiDonkey/DelphiAnthropic --skill delphi-uses-graphgit clone --depth 1 https://github.com/MaxiDonkey/DelphiAnthropicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maxidonkey/delphianthropic/delphi-uses-graph)<a href="https://agentmods.dev/skills/maxidonkey/delphianthropic/delphi-uses-graph"><img src="https://agentmods.dev/badge/skills/maxidonkey/delphianthropic/delphi-uses-graph/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maxidonkey/delphianthropic/delphi-uses-graph"><img src="https://agentmods.dev/badge/skills/maxidonkey/delphianthropic/delphi-uses-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00124 | $0.00944 |
| Opus 5 | $0.00062 | $0.00472 |
| Sonnet 5 | $0.00025 | $0.00189 |
| Haiku 4.5 | $0.00012 | $0.00094 |
Grade A, and why
delphi-uses-graph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
When to use this skill
- The user uploads an archive (
.zip,.tar,.tar.gz) containing a Delphi project, or a folder of.pas/.dpr/.dpksources, 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 embed, 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 (preferred). - A directory already containing
.pas,.dpr,.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 subfolder of the working area. - Read the generated
report.mdand summarize findings to the user: number of parsed units, top fan-in / fan-out, cycles, orphan units. - Attach the produced artifacts (Mermaid, DOT, SVG when present, JSON,
report) to the response. Embed
uses-graph.mmdinline in the answer so the user sees the diagram rendered.
Quick start
python scripts/tool.py \
--input /path/to/project.zip \
--output /path/to/out
Useful flags (full list in reference.md):
--scope {all,interface,implementation}— whichusessections to consider. Defaultall.--ignore-prefix LIST— drop edges whose target starts with one of these comma-separated prefixes, case-insensitive. DefaultSystem,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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 84 lines · 124 tokens per session scan A 9cb170d96dcd
delphi-uses-graph is a skill published in the GitHub repository MaxiDonkey/DelphiAnthropic (60 stars, last pushed 2d ago), licensed MIT. It adds 124 tokens to every session and 944 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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