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 badass-data-science/pyFit-agentic-polygon-nesting --skill pyfitgit clone --depth 1 https://github.com/badass-data-science/pyFit-agentic-polygon-nestingWrote 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/badass-data-science/pyfit-agentic-polygon-nesting/pyfit)<a href="https://agentmods.dev/skills/badass-data-science/pyfit-agentic-polygon-nesting/pyfit"><img src="https://agentmods.dev/badge/skills/badass-data-science/pyfit-agentic-polygon-nesting/pyfit/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/badass-data-science/pyfit-agentic-polygon-nesting/pyfit"><img src="https://agentmods.dev/badge/skills/badass-data-science/pyfit-agentic-polygon-nesting/pyfit.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 | $0.00074 | $0.00959 |
| Opus 5.5 | $0.00030 | $0.00384 |
| Sonnet 5.5 | $0.00015 | $0.00192 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
pyfit 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pyfit is a general-purpose 2D irregular-polygon nesting (bin-packing) tool: given a set of 2D shapes and how many of each are needed, it arranges them onto rectangular sheet stock with minimal wasted material, via a no-fit-polygon bottom-left-fill heuristic. It reads part outlines from DXF files or inline polygons and writes one ready-to-cut DXF file per sheet used, plus a JSON placement report.
Not installed as a bundled OpenClaw skill dependency — install it first:
pip install pyfit-agentic-polygon-nesting
(The distribution name is pyfit-agentic-polygon-nesting; the installed
command is pyfit.)
When to use this
Reach for pyfit whenever the user wants to:
- Figure out how to arrange/pack/nest a set of 2D shapes onto sheet stock with minimal wasted material.
- Know how many copies of a shape fit on a sheet of a given size.
- Produce cut-ready DXF files for a laser cutter or CNC router from a list of part outlines.
Running a job
-
Write a job spec JSON file describing the sheet size and the parts to nest:
{ "sheet": {"width": 96, "height": 48}, "parts": [ {"name": "shapeA", "dxf": "facetype1.dxf", "quantity": 120, "allow_mirror": true}, {"name": "shapeB", "polygon": [[0, 0], [1, 0], [0, 1]], "quantity": 10, "allow_mirror": false} ] }Each part gives its outline either as
"dxf"(a path to a DXF file containing exactly one closed loop) or"polygon"(an inline list of[x, y]points), a"quantity", and optionally"allow_mirror"(defaulttrue; setfalsefor chirality-sensitive material, e.g. wood grain or a printed pattern, where a mirror-image placement isn't interchangeable with the original). -
Run it:
pyfit -j job.json -o output/nestWrites
output/nest_sheet1.dxf,output/nest_sheet2.dxf, ... (one file per sheet actually used) plusoutput/nest_report.json, and prints the same report as JSON to stdout. Add-P/--previewto also write a quick 2D preview PNG per sheet (output/nest_sheet1.png, ...) for a fast sanity check without opening a DXF viewer.
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 · 91 lines · 74 tokens per session scan A cf561658fb19
pyfit is a skill published in the GitHub repository badass-data-science/pyFit-agentic-polygon-nesting (0 stars, last pushed 3mo ago), licensed MIT. It adds 74 tokens to every session and 959 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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