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.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/data-auditor-cleaner/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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.
[](https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/data-auditor-cleaner)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.00931 |
| Opus 5 | $0.00023 | $0.00465 |
| Sonnet 5 | $0.00009 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
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 11d 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.
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
-
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.
-
Preserve raw data.
- Treat raw files as read-only.
- Record hashes or stable file metadata when practical.
- Write cleaned copies under
workspace/data_clean/.
-
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.
-
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.
-
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.
-
Assess readiness per Qx.
ready,ready_with_warnings, orblocked.- Name missing fields and risks precisely.
- Hand the profile to
method-selectorfor method-specific risk probes.
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.
- 11d ago First seen · 120 lines · 45 tokens per session scan A 541f1f225560
data-auditor-cleaner is a skill published in the GitHub repository zhnnky329/MathModeling-skills (847 stars, last pushed 17d ago), licensed MIT. It adds 45 tokens to every session and 931 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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