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 chengziyue1222/math-model-agent --skill make-model-figuresgit clone --depth 1 https://github.com/chengziyue1222/math-model-agentWrote 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/chengziyue1222/math-model-agent/make-model-figures)<a href="https://agentmods.dev/skills/chengziyue1222/math-model-agent/make-model-figures"><img src="https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/make-model-figures.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.00565 |
| Opus 5 | $0.00036 | $0.00282 |
| Sonnet 5 | $0.00015 | $0.00113 |
| Haiku 4.5 | $0.00007 | $0.00056 |
Grade A, and why
make-model-figures 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 8d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Make Model Figures
Generate figures that prove a particular modeling conclusion. A chart is not included merely because there is data available.
Workflow
- Start from a conclusion, validation claim or mechanism that the paper must establish.
- Read
references/paper-figure-language.md,references/figure-standards.mdand the relevant functions incode/algorithms/sci_figures.pyordiagram.py. - Select the smallest appropriate form: route diagram, mechanism sketch, snapshots, global-plus-inset, trend/threshold, sensitivity, convergence or a representative comparison table. Record why competing forms were not needed.
- Plan panels and final A4 dimensions before plotting. One page should not contain four unreadable mini-plots; no run of pages should become pure figures without explanatory text.
- Plot from saved data/results. Label variables and units; show reference values, thresholds, feasible regions, uncertainty or baseline whenever these are needed for the conclusion.
- Apply
paper_figure_rc_params(orpublication_rc_paramswhere venue settings require it), export vector masters and inspect the rendered PDF-size proof. - For every formal figure, record source data, model/scenario, claim, units, filtering, final size and any randomness. This metadata is for audit, not reader-facing prose.
- In the manuscript, introduce why the figure is needed and explain its pattern, mechanism and limitation after it.
Style
Use white backgrounds, muted semantic colors, one restrained emphasis color, weak grids and readable Chinese/Latin labels. Avoid rainbow maps, decorative 3-D effects, generic dashboard palettes, obscuring legends and unlabeled axes.
Resources
references/paper-figure-language.md— conclusion-to-figure grammar.references/figure-standards.md— sizing and export rules.references/publication-workflow.md— reproducibility and rendered inspection.
Executable Contract
For repository-managed competition and audit projects, inspect the shared contract registry with python -m scripts.skill_contracts --skill make-model-figures and run this Skill through the local scripts/execute_skill.py with every contracted input and output role. In rapid, retain the plotted source data, claim, units, and generation parameters; reserve formal registry and PDF-size audit work for a stricter profile.
What ships with it
8 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 282 B
- references/figure-standards.md 1.5 KB
- references/paper-figure-language.md 1.3 KB
- references/publication-workflow.md 3.2 KB
- references/rule-pack-integration.md 569 B
- scripts/execute_skill.py 232 B runs code
- scripts/render_process_figures.py 6.9 KB runs code
- scripts/render_supplier_figures.py 8.5 KB runs code
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.
- 8d ago First seen · 34 lines · 73 tokens per session scan A 33d2c9171688
make-model-figures is a skill published in the GitHub repository chengziyue1222/math-model-agent (16 stars, last pushed 27d ago), licensed MIT. It adds 73 tokens to every session and 565 once invoked, about $0.0004 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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