zhnnky329/mathmodeling-skills/python-model-code-generator
Claude Code and agent skill by zhnnky329/mathmodeling-skills
- community
Checking that the SKILL.md file is still in the repository.
Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.
Install
npx vskill@latest install zhnnky329/mathmodeling-skills/python-model-code-generatorSKILL.md
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.
- 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:
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.
Verification
- Main and baseline both ran and are directly comparable.
- Fallback code is absent unless activated.
- Formal outputs and run summary exist.
- Seed, inputs, versions, warnings, and errors are recorded.
- Required concentration/degeneracy checks are saved.
- Next handoff is
code-reviewer.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugins/mathmodeling-skills/skills/python-model-code-generator/SKILL.md
Popularity
- Repository stars
- 1,060
- Repository forks
- 42
- 7-day trend score
- 40
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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