MathModeling-skills is a collection of Claude Code and Codex skills for mathematical modeling competitions, guiding users through data analysis, method-specific checks, reproducible computations, figures, and paper audits without taking over modeling decisions. It is for contestants who want AI to handle mechanical checks while they retain responsibility for choosing methods and interpreting results. The catalogue entries form the project's skills, instructions, hook, setting, and plugin workflow.
Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/python-model-code-generator/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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/zhnnky329/mathmodeling-skills/python-model-code-generator)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/python-model-code-generator"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/python-model-code-generator/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/zhnnky329/mathmodeling-skills/python-model-code-generator"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/python-model-code-generator.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 | $0.00034 | $0.00617 |
| Opus 5.5 | $0.00014 | $0.00247 |
| Sonnet 5.5 | $0.00007 | $0.00123 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
python-model-code-generator 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 2mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preconditions
- G2.5 human method choice is recorded in
methods/Qx/qx_decisions.jsonl. code/Qx/qx_code_plan.mdexists.- Required cleaned data and profile exist.
- The plan targets Python.
Legacy method pools and code/model-code-analyzer.md may be read during migration, but they do not override the human choice.
Workflow
- Read the code plan, decision ledger, method card, probe conditions, and data profile.
- Confirm scope:
- one approved main method;
- one usable baseline;
- fallback only when an activation decision or evidenced trigger exists.
- Generate clear runnable
.pyfiles undercode/Qx/. - Use project-root-safe paths, fixed seeds, explicit inputs, and minimal justified dependencies.
- Save:
- tables to
results/Qx/experiments/roundN/tables/; - metrics to
.../metrics/; - useful diagnostic/comparison figures to
.../figures/; - canonical
run_summary.json.
- tables to
- Evaluate and record output-degeneracy and fallback-trigger metrics required by the plan.
- Persist full logs only on failure or when a warning needs reproduction.
- Run the code. Do not claim success from code generation alone.
- Hand off to
code-reviewer.
Script Layout
Prefer the smallest clear layout:
code/Qx/
├── qx_code_plan.md
├── qx_baseline.py
├── qx_main.py
└── run_all.py # only when coordination is useful
Do not create one script per unapproved candidate. Do not create a README that duplicates the code plan.
Run Summary
Follow the schema in model-code-analyzer. Include:
- approved decision ID;
- method IDs and roles;
- inputs and outputs;
- seed and environment;
- execution status and timing;
- compact metric summaries;
- output-degeneracy evidence;
- warnings/errors;
- fallback-trigger state.
Rules
- Do not change the approved model or baseline.
- Do not read or overwrite raw data.
- Do not hide assumptions in code.
- Do not emit placeholder metrics, figures, or successful statuses.
- Prefer portable
.pyscripts over notebook-only workflows. - Keep intermediate files only when needed for explanation, review, robustness, or debugging.
- Use Type 1 diagnostic figures internally; do not present them as paper figures.
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.
- 2mo ago First seen · 80 lines · 34 tokens per session scan A d2877a8b66da
python-model-code-generator is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 617 once invoked, about $0.0001 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 2026-08-30.
python-model-code-generator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| python-model-code-generatorzhnnky329/MathModeling-skills | Skill | 1.1k | 1mo ago | MIT | A | 34 | Claude Code |
| jupyter-notebookNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| pennylaneK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 74 | Claude Code |
| cuequivariance-jaxNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 38 | Claude Code, Codex |
| cuequivariance-torchNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 37 | Claude Code, Codex |
| cuopt-numerical-optimization-apiNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 51 | Claude Code, Codex |