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MathModeling-skills: Skill for Claude Code

.claude/skills/data-auditor-cleaner/SKILL.md

data-auditor-cleaner is a skill for Claude Code from zhnnky329/MathModeling-skills. It costs 45 tokens per session (931 once invoked), scanned A, original, MIT.

A data-auditing workflow that maps contest files to questions, checks and cleans the raw data, and creates one reusable profile of its quality and suitability for analysis.

In plain words
What is it for?
Use it to inventory attachments, preserve raw files, create cleaned copies, check structure and meaning, measure missingness and imbalance, and document which fields support each question.
Why use it?
It prevents missing files, unclear fields, bad values, duplicates, data leakage, and repeated inspections from undermining later work.

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 →

not rated 1.1krepo +7 1mo ago A scan SkillSpector: pass 45 tokens original MIT
View source ↗ zhnnky329/MathModeling-skills
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/data-auditor-cleaner/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

Made for: Claude Code.

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 data-auditor-cleaner

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner/github.svg)](https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner)
Your own site
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner/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.

agentmods 80×15 button for data-auditor-cleaner

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 931 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 $0.00045 $0.00931
Opus 5.5 $0.00018 $0.00372
Sonnet 5.5 $0.00009 $0.00186
Haiku 4.5 $0.00005 $0.00093

Measured 2mo ago against content hash 541f1f225560, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-auditor-cleaner 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/data-auditor-cleaner/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

Create traceable cleaned data and one reusable profile. Do not repeat the same data inspection separately for every candidate method.

Preconditions

  • Problem parse and subquestion IDs exist.
  • Raw files are available under workspace/data_raw/ or the workspace's documented legacy raw-data path.
  • Required outputs and known field needs are available.

Stop rather than fabricate a missing attachment, unit, field meaning, or label.

Workflow

  1. Map attachments before cleaning.

    • List each attachment with name, size, sheet names, headers, and a small preview.
    • Map it to Qx or mark it shared.
    • Ask the user only when two mappings remain materially plausible.

  2. Preserve raw data.

    • Treat raw files as read-only.
    • Record hashes or stable file metadata when practical.
    • Write cleaned copies under workspace/data_clean/.

  3. Audit structure and semantics.

    • Rows, columns, keys, types, units, categories, time granularity, and encoding.
    • Missing values, duplicates, impossible values, outliers, discontinuities, and leakage risks.
    • Field-to-subquestion and field-to-required-output mapping.

  4. Compute reusable risk-profile statistics.

    • Effective sample size and rows usable per Qx.
    • Missingness by field and row.
    • Numeric distribution summaries and extreme-value rates.
    • Category/class counts, imbalance ratios, rare levels, and cardinality.
    • Time coverage, gaps, sampling interval, and chronological split constraints.
    • Correlation/redundancy warnings where relevant.
    • Target or score concentration indicators when a target exists.
    • Record facts; do not convert them into a final method verdict.

  5. Plan and apply cleaning.

    • Separate safe normalization of representation from assumption-bearing imputations or removals.
    • Explain and record every assumption-bearing operation.
    • Keep reproducible cleaning code only when transformations are nontrivial.

  6. Assess readiness per Qx.

    • ready, ready_with_warnings, or blocked.
    • Name missing fields and risks precisely.
    • Hand the profile to method-selector for method-specific risk probes.

Read the full file on GitHub · 120 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 · 120 lines · 45 tokens per session scan A 541f1f225560

Subscribe to this mod's changes

data-auditor-cleaner is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 931 once invoked, about $0.0002 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.

Analytics and metricsData and AI
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