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.
npx skills add seb1n/awesome-ai-agent-skills --skill data-cleaninggit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/data-cleaning)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/data-cleaning"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/data-cleaning/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/seb1n/awesome-ai-agent-skills/data-cleaning"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/data-cleaning.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 | $0.00043 | $0.01837 |
| Opus 5 | $0.00022 | $0.00919 |
| Sonnet 5 | $0.00009 | $0.00367 |
| Haiku 4.5 | $0.00004 | $0.00184 |
Grade A, and why
data-cleaning 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-cleaning — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Cleaning
This skill enables an AI agent to systematically clean and preprocess raw datasets into analysis-ready form. The agent handles missing values, duplicate records, data type mismatches, inconsistent formats, outlier treatment, and normalization. It can also enforce validation schemas to ensure ongoing data quality. The primary toolchain is pandas with support from pyjanitor and great_expectations for advanced validation.
Workflow
-
Ingest and profile the raw data. Load the dataset and immediately generate a quality report: count nulls per column, identify duplicate rows, check data types against expected schema, and flag columns with mixed types. This profile drives every subsequent cleaning decision.
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Handle missing values. Apply strategy per column based on data type and missingness pattern. For numeric columns with less than 5% missing, use median imputation. For categorical columns, use mode or a dedicated "Unknown" category. For columns missing more than 40%, flag them for potential removal and consult the user before dropping.
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Remove duplicates and resolve conflicts. Identify exact duplicates and near-duplicates (e.g., rows differing only in whitespace or casing). For exact duplicates, keep the first occurrence. For near-duplicates, apply fuzzy matching with a configurable similarity threshold and merge conflicting values by recency or completeness.
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Correct data types and standardize formats. Coerce columns to their intended types — parse date strings into datetime objects, convert numeric strings to floats, and normalize categorical values to a canonical form. Standardize formats such as phone numbers, postal codes, and currency representations.
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Detect and treat outliers. Use the IQR method (1.5x) for symmetric distributions and z-scores for normally distributed data. Offer three treatment options: cap at boundary values (winsorization), replace with null for later imputation, or flag-only mode that annotates but preserves original values.
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 · 154 lines · 43 tokens per session scan A 527a4e3e1198
data-cleaning is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,837 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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