python-code-reviewer

A reviewer for Python code used in mathematical modeling. It checks the code against its approved plan, inputs, chosen method, and experiment results.

In plain words
What is it for?
Use it to inspect, run, debug, and verify approved modeling code, including its data handling, repeatability, metrics, figures, and fallback behavior.
Why use it?
It can expose syntax errors, incorrect inputs, mismatched methods, poor reproducibility, and missing or invalid outputs before results are trusted.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/zhnnky329/mathmodeling-skills/python-code-reviewer
Any agent
npx skills add zhnnky329/MathModeling-skills --skill python-code-reviewer
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

Made for: Claude Code, Codex.

Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 637 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00041 $0.00637
Opus 5 $0.00020 $0.00318
Sonnet 5 $0.00008 $0.00127
Haiku 4.5 $0.00004 $0.00064

Measured 3d ago against content hash 5927bebd330b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-code-reviewer 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 3d 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-code-reviewer/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preconditions

  • Python code and code/Qx/qx_code_plan.md exist.
  • Approved method decision, method card, data profile, and relevant run summary are available.
  • Required inputs are accessible.

Workflow

  1. Resolve the approved main and usable baseline. Flag scripts for unapproved candidates unless a fallback activation exists.
  2. Inspect and run the code in the intended order.
  3. Evaluate required checks:
    • syntax: imports, execution, exceptions, and obvious runtime faults.
    • input_contract: paths, fields, units, shapes, missing-data handling, and raw-data protection.
    • method_alignment: formulas, objectives, constraints, assumptions, main/baseline roles, and fallback scope match the approved plan.
    • reproducibility: seed, deterministic setup, dependency/runtime record, and rerun consistency.
    • output_contract: saved tables/metrics/figures, valid run summary, comparable main/baseline metrics, degeneracy evidence, and fallback-trigger state.
  4. Add risk-specific checks only when relevant, such as leakage, constraint feasibility, numerical stability, or scale.
  5. If asked to fix findings, make minimal changes, rerun affected checks, and record the repair. Otherwise report findings without changing code.
  6. Save code/Qx/reviews/qx_python_review.json.

Review Schema

{
  "schema_version": 1,
  "question_id": "Q1",
  "language": "python",
  "reviewed_files": [],
  "decision_id": "q1_method_choice",
  "checks": {
    "syntax": {"status": "PASS", "evidence": []},
    "input_contract": {"status": "PASS", "evidence": []},
    "method_alignment": {"status": "PASS", "evidence": []},
    "reproducibility": {"status": "PASS", "evidence": []},
    "output_contract": {"status": "PASS", "evidence": []}
  },
  "findings": [],
  "verdict": "PASSED",
  "reviewed_at": "ISO-8601"
}

Statuses are PASS, FAIL, or NOT_APPLICABLE with a reason. Any required FAIL blocks G3.

Rules

  • Do not pad evidence to reach a count.
  • Do not fabricate execution or outputs.
  • Do not approve a toy diagnostic reference as the official baseline.
  • Do not silently change mathematical meaning.
  • Do not create success logs beyond the review JSON.
  • Treat code newer than its review as requiring the affected checks to rerun, not necessarily the entire pipeline.

Read the full file on GitHub · 65 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. 3d ago First seen · 65 lines · 41 tokens per session scan A 5927bebd330b

Subscribe to this mod's changes

python-code-reviewer is a skill published in the GitHub repository zhnnky329/MathModeling-skills (682 stars, last pushed 8d ago), licensed MIT. It adds 41 tokens to every session and 637 once invoked, about $0.0002 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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