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 agentmods add skills/jayrha/agentskills/pandas-data-cleaningnpx skills add JayRHa/AgentSkills --skill pandas-data-cleaninggit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/pandas-data-cleaning)<a href="https://agentmods.dev/skills/jayrha/agentskills/pandas-data-cleaning"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/pandas-data-cleaning.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00127 | $0.01972 |
| Opus 5 | $0.00063 | $0.00986 |
| Sonnet 5 | $0.00025 | $0.00394 |
| Haiku 4.5 | $0.00013 | $0.00197 |
Grade A, and why
pandas-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 4d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pandas Data Cleaning
Overview
Keywords: pandas, data cleaning, dtypes, missing values, NaN, imputation, duplicates, outliers, IQR, z-score, tidy data, melt, pivot, normalize, standardize, parse dates, categorical, data quality, ETL preprocessing.
This skill turns a messy DataFrame into a tidy, correctly-typed, analysis-ready dataset using a repeatable, auditable workflow. The core principle: profile first, decide explicitly, transform with logging, validate after. Never mutate data silently — every fill, drop, or cast should be a deliberate, documented choice you can defend.
Treat cleaning as a pipeline that produces (1) the cleaned DataFrame and (2) a record of decisions. Prefer chained, non-mutating transforms (df.assign(...), .pipe(...)) over scattered in-place edits so the pipeline is reproducible top-to-bottom.
Workflow
-
Profile the raw data. Before changing anything, understand it. Run
scripts/profile_data.py <path>(or replicate inline) to get shape, dtypes, per-column null counts/percentages, unique counts, sample values, and candidate problems (mixed types, high-cardinality strings, numeric-looking objects, constant columns). Seereferences/cleaning-checklist.md. -
Fix structure. Standardize column names (snake_case, strip whitespace, dedupe). Set/verify the index. Drop fully-empty rows/columns and constant columns that carry no signal. Confirm one observation per row, one variable per column (tidy form). If not tidy, defer reshape to step 8.
-
Coerce dtypes. Convert numeric-looking strings (
"1,234","$5.00","12%") to numbers, parse dates with explicit formats, cast low-cardinality strings tocategory, and use nullable dtypes (Int64,boolean,string) where missing values must coexist with non-float types. Seereferences/dtype-conversion.md. -
Standardize text & categories. Trim whitespace, normalize case, collapse synonyms ("USA"/"U.S.A."/"United States"), fix encoding artifacts, and map free-text categories to a controlled vocabulary.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 99 lines · 127 tokens per session scan A 294680269b04
pandas-data-cleaning is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 127 tokens to every session and 1,972 once invoked, about $0.0006 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 2026-08-31.
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