model-code-analyzer

model-code-analyzer is a skill for Claude Code, Codex from zhnnky329/MathModeling-skills. It costs 41 tokens per session (790 once invoked), scanned A, original, MIT.

A planning tool that turns a human-approved mathematical method and baseline into a language-neutral coding and experiment specification.

In plain words
What is it for?
Use it to define implementation steps, comparison metrics, validation checks, fallback conditions, and the folder structure for experiment results.
Why use it?
It prevents code generation from drifting beyond the chosen experiment and makes the required inputs, outputs, checks, and saved results explicit.

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/model-code-analyzer
Any agent
npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for model-code-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/model-code-analyzer.svg)](https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/model-code-analyzer)
Your own site
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/model-code-analyzer"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/model-code-analyzer.svg" alt="Measured on agentmods" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 790 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 $0.00790
Opus 5 $0.00020 $0.00395
Sonnet 5 $0.00008 $0.00158
Haiku 4.5 $0.00004 $0.00079

Measured 5d ago against content hash 6b68d5e1bc78, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

model-code-analyzer 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 5d 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/model-code-analyzer/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

Define exactly what code must implement and save. Do not expand the approved experiment scope or fully plan a dormant fallback.

Preconditions

  • methods/Qx/qx_method_card.md and probe summary exist.
  • methods/Qx/qx_decisions.jsonl contains a human DECIDED method choice.
  • A usable baseline is identified.
  • Cleaned data and data_profile.json are ready when data is required.
  • Implementation target and round are known.

Read legacy candidate/decision artifacts only when the new artifacts are absent.

Workflow

  1. Read the approved choice, method card, probe conditions, and experiment budget.
  2. Plan only:
    • approved main;
    • approved usable_baseline;
    • shared helpers and comparison logic.
  3. Record the fallback ID and trigger, but do not plan its full implementation unless the trigger is already evidenced and the human chose activation.
  4. Map mathematical definitions to inputs, processing steps, intermediate evidence, outputs, and validation checks.
  5. Define a directly comparable metric/output contract for main and baseline.
  6. Define the round output:
results/Qx/experiments/roundN/
├── figures/
├── tables/
├── metrics/
└── run_summary.json

Create logs/ only for failures, warnings, or reproducibility needs. 7. Write code/Qx/qx_code_plan.md for Python or code/matlab/Qx/qx_code_plan.md for MATLAB. 8. Hand off to the matching language generator.

Run Summary Contract

Require:

{
  "schema_version": 1,
  "question": "Q1",
  "round": "round1",
  "implementation_target": "python",
  "random_seed": 2026,
  "approved_decision_id": "q1_method_choice",
  "methods": [
    {
      "method_id": "M1",
      "role": "usable_baseline",
      "script": "code/Q1/q1_baseline.py",
      "status": "success",
      "execution_time_seconds": 0,
      "input_files": [],
      "output_files": [],
      "figure_files": [],
      "metrics_summary": {},
      "warnings": [],
      "errors": []
    }
  ],
  "comparison": {},
  "fallback_trigger": {
    "fallback_id": null,
    "condition": null,
    "observed": false,
    "evidence": null
  },
  "environment": {}
}

Read the full file on GitHub · 112 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. 5d ago First seen · 112 lines · 41 tokens per session scan A 6b68d5e1bc78

Subscribe to this mod's changes

model-code-analyzer is a skill published in the GitHub repository zhnnky329/MathModeling-skills (716 stars, last pushed 11d ago), licensed MIT. It adds 41 tokens to every session and 790 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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