MathModeling-skills: Skill for Claude Code

.claude/skills/python-model-code-generator/SKILL.md

python-model-code-generator is a skill for Claude Code from zhnnky329/MathModeling-skills. It costs 34 tokens per session (617 once invoked), scanned A, original, MIT.

A workflow for generating and running small Python programs that test an approved research model and a baseline comparison. It saves the code, measurements, tables, charts, and run summary in the project’s experiment folders.

In plain words
What is it for?
Use it to create model code, run experiments with fixed inputs and seeds, save results and diagnostics, measure degenerate outputs, and prepare the work for code review.
Why use it?
It turns a recorded method choice into a reproducible experiment and confirms that the generated code actually runs.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is zhnnky329/MathModeling-skills's own configuration. It tells Claude Code how to work on MathModeling-skills itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything MathModeling-skills configures →

About the project

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.

zhnnky329/MathModeling-skills · 1,080 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/python-model-code-generator/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

Made for: Claude Code.

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README.md
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<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>

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Your own site · 80×15
<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>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 617 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2mo ago against content hash d2877a8b66da, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.claude/skills/python-model-code-generator/SKILL.md · 80 lines

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.md exists.
  • 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

  1. Read the code plan, decision ledger, method card, probe conditions, and data profile.
  2. Confirm scope:
    • one approved main method;
    • one usable baseline;
    • fallback only when an activation decision or evidenced trigger exists.
  3. Generate clear runnable .py files under code/Qx/.
  4. Use project-root-safe paths, fixed seeds, explicit inputs, and minimal justified dependencies.
  5. Save:
    • tables to results/Qx/experiments/roundN/tables/;
    • metrics to .../metrics/;
    • useful diagnostic/comparison figures to .../figures/;
    • canonical run_summary.json.
  6. Evaluate and record output-degeneracy and fallback-trigger metrics required by the plan.
  7. Persist full logs only on failure or when a warning needs reproduction.
  8. Run the code. Do not claim success from code generation alone.
  9. 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 .py scripts 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.

Read the full file on GitHub · 80 lines

Changes

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.

  1. 2mo ago First seen · 80 lines · 34 tokens per session scan A d2877a8b66da

Subscribe to this mod's changes

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.

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