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 Peter-N91/hve-squad --skill python-diagramsgit clone --depth 1 https://github.com/Peter-N91/hve-squadWrote 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/peter-n91/hve-squad/python-diagrams)<a href="https://agentmods.dev/skills/peter-n91/hve-squad/python-diagrams"><img src="https://agentmods.dev/badge/skills/peter-n91/hve-squad/python-diagrams/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/peter-n91/hve-squad/python-diagrams"><img src="https://agentmods.dev/badge/skills/peter-n91/hve-squad/python-diagrams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.01761 |
| Opus 5.5 | $0.00033 | $0.00704 |
| Sonnet 5.5 | $0.00016 | $0.00352 |
| Haiku 4.5 | $0.00008 | $0.00176 |
Grade A, and why
python-diagrams 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 1mo 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Diagrams
Overview
This skill bundles a documentation-only reference template for rendering Azure architecture
diagrams with real Azure product icons using the Python diagrams
library on a Graphviz backend. It is the file-based, committed-artifact counterpart to the squad's
Mermaid default and the interactive drawio MCP path: where Mermaid renders inline and draw.io opens
an editor, this skill writes paired .png + .svg siblings straight into the consumer repo.
Like every other reference template in the squad package (see .github/skills/azure-scaffold/ and
.github/skills/squad/), nothing here runs from the package. The consumer copies a generator into
their repo, adjusts it to match their Bicep or Terraform modules, and runs it. The package never reads
or writes the consumer's source tree.
This skill backs the diagram-rendering capability's non-MCP, file-output option documented in
squad-mcp-capability.instructions.md
("Out-of-Band Fallbacks → Python diagrams Library"). The library is the integration surface; no
.vscode/mcp.json entry is required.
When to use
- A role (typically the Squad Azure Architect) needs an HLD or LLD diagram with Azure icons persisted as a committed image, not an inline Mermaid block or a browser-only draw.io tab.
- You want the diagram to live next to the IaC and stay diffable in version control.
Does not: produce Mermaid (author that inline), drive the drawio MCP (that is the interactive
draw.io path), generate Bicep/Terraform, or deploy anything.
Bundled files
| File | Consumer destination (suggested) | Purpose |
|---|---|---|
| requirements.txt | merged into the repo's Python deps | Pins diagrams and graphviz; notes the Graphviz system prerequisite. |
| scripts/diagram_io.py | beside each generator (or on sys.path) |
Shared output helper enforcing the paired PNG + SVG dual-output contract. |
| scripts/verify_installation.py | run once after install | Confirms diagrams imports and Graphviz dot renders. |
| templates/azure-webapp-lld.py | docs/architecture/azure_webapp_lld.py |
Azure internal web app LLD generator with real Azure icons. |
What ships with it
4 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.
- 1mo ago First seen · 140 lines · 82 tokens per session scan A 779ad267d867
python-diagrams is a skill published in the GitHub repository Peter-N91/hve-squad (13 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 1,761 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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