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 sweetcornna/mathodology --skill mathodology-figure-presetsgit clone --depth 1 https://github.com/sweetcornna/mathodologyWrote 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/sweetcornna/mathodology/mathodology-figure-presets)<a href="https://agentmods.dev/skills/sweetcornna/mathodology/mathodology-figure-presets"><img src="https://agentmods.dev/badge/skills/sweetcornna/mathodology/mathodology-figure-presets/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/sweetcornna/mathodology/mathodology-figure-presets"><img src="https://agentmods.dev/badge/skills/sweetcornna/mathodology/mathodology-figure-presets.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 | $0.00032 | $0.00839 |
| Opus 5.5 | $0.00013 | $0.00336 |
| Sonnet 5.5 | $0.00006 | $0.00168 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade C, and why
mathodology-figure-presets scanned grade C with 1 finding 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.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
cases. Do not claim a generic image tool is image2 or collect API keys in chat. How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mathodology Scientific Figure Presets
Choose a figure from the question the reader needs to answer. Complex figures are useful when their panels expose related evidence; complexity itself is not a quality measure. Use actual data and the user's paper language.
Agent-facing guidance
Read default figure guidance when starting figure work or briefing another agent. It is the canonical reusable instruction: select a code template, fill real data, run it and inspect the resulting PNG/PDF. Use it as adaptable guidance, not a fixed pipeline.
Before the first figure
Ask once per modeling task, unless the answer is already known:
是否有可用的 image2 模型？通过当前工具、已配置接口，还是手动生成使用？
Use the host's question mechanism and continue independent analysis while waiting. Do not repeat the question in every specialist or turn. Read image2 usage for available, manual, unavailable and pending cases. Do not claim a generic image tool is image2 or collect API keys in chat.
Default rendering route
Without image2, with a pending answer, or with only manual image2 access, use the 20 callable code templates by default. Copy the matching function into the working task, bind actual data, execute it, and export PNG/PDF. Do not stop at a plotting prompt or wait for image2. Quantitative figures use this same data-driven route even when image2 is available. Adapt the closest template when needed; never substitute synthetic preview values for missing data.
Choose and build
- Write the figure's intended conclusion as a question before seeing the result. Identify data, units, comparison, uncertainty and the available paper space.
- Use the 20-preset selector and recipes. Load the relevant cards, not the entire reference collection. If the data cannot support a preset, choose its simpler alternative or explain the missing input.
- Adapt the card's plotting and caption prompts using the actual columns, measured results and scientific meaning. Do not force the data to match a preview. Follow style and export guidance.
- Use numerical plotting tools for quantitative marks. image2 can assist illustrations, mechanism diagrams and layout concepts; rebuild quantitative layers from data. Never use generated pixels as computed evidence.
- Inspect the rendered figure at publication size, then inspect its placement in the compiled paper. Fix illegibility and misleading encodings; remove decorative panels that do not support the argument.
What ships with it
38 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.
- agents/openai.yaml 639 B
- examples/f01-mosaic.pdf 28 KB
- examples/f01-mosaic.png 104 KB
- examples/f02-forecast.pdf 25 KB
- examples/f02-forecast.png 89 KB
- examples/f03-raincloud.pdf 39 KB
- examples/f03-raincloud.png 71 KB
- examples/f10-pareto.pdf 28 KB
- examples/f10-pareto.png 64 KB
- examples/f16-sensitivity.pdf 28 KB
- examples/f16-sensitivity.png 42 KB
- examples/f20-scenarios.pdf 28 KB
- examples/f20-scenarios.png 65 KB
- examples/README.md 5.0 KB
- references/figure-guidance.md 2.6 KB
- references/image2.md 4.6 KB
- references/matplotlib/confidence_ellipse.py.txt 6.6 KB
- references/matplotlib/contourf_demo.py.txt 4.2 KB
- references/matplotlib/fill_between_demo.py.txt 4.5 KB
- references/matplotlib/image_annotated_heatmap.py.txt 10 KB
- references/matplotlib/inset_locator_demo2.py.txt 2.3 KB
- references/matplotlib/LICENSE.txt 4.7 KB
- references/matplotlib/sankey_basics.py.txt 3.5 KB
- references/matplotlib/scatter_hist.py.txt 4.5 KB
- references/matplotlib/violinplot.py.txt 4.8 KB
- references/papers/plos-figure-3.png 86 KB
- references/papers/plos-figure-4.png 34 KB
- references/papers/plos-figure-5.png 111 KB
- references/papers/plos-figure-6.png 26 KB
- references/papers/plos-figure-7.png 157 KB
- references/papers/plos-figure-8.png 54 KB
- references/presets.md 26 KB
- references/README.md 4.6 KB
- references/sources.tsv 5.2 KB
- references/style.md 3.1 KB
- scripts/render_examples.py 11 KB runs code
- templates/matplotlib_templates.py 24 KB runs code
- templates/README.md 6.3 KB
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 · 79 lines · 32 tokens per session scan C 0340786508b7
mathodology-figure-presets is a skill published in the GitHub repository sweetcornna/mathodology (308 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 839 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (harvests environment variables). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-07.
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