Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/georgekhananaev/claude-skills-vaultnpx agentmods add skills/georgekhananaev/claude-skills-vault/data-wranglerWrote 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/georgekhananaev/claude-skills-vault/data-wrangler)<a href="https://agentmods.dev/skills/georgekhananaev/claude-skills-vault/data-wrangler"><img src="https://agentmods.dev/badge/skills/georgekhananaev/claude-skills-vault/data-wrangler/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/georgekhananaev/claude-skills-vault/data-wrangler"><img src="https://agentmods.dev/badge/skills/georgekhananaev/claude-skills-vault/data-wrangler.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 | $0.00095 | $0.02792 |
| Opus 5.5 | $0.00038 | $0.01117 |
| Sonnet 5.5 | $0.00019 | $0.00558 |
| Haiku 4.5 | $0.00010 | $0.00279 |
Grade A, and why
data-wrangler 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 1mo 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Wrangler
Manipulate tabular data (CSV, Excel, JSON, Parquet, TSV) w/ pandas-powered scripts. Two scripts cover all operations: data_wrangler.py for data ops, excel_toolkit.py for Excel-specific features.
When to Use
- User asks to read, edit, filter, sort, or transform CSV/Excel/JSON/Parquet/TSV files
- User asks to merge/join datasets, deduplicate, fill missing values, or validate data
- User asks to create Excel workbooks w/ formatting, dropdowns, freeze panes, or multi-sheet
- User asks to pivot, unpivot, group-by, aggregate, sample, or split datasets
- User asks to add computed columns, rename columns, cast types, or apply formulas
- User asks to convert between data formats (CSV -> Excel, JSON -> Parquet, etc.)
- User asks to inspect/profile data structure, types, nulls, stats
Prerequisites
# Required
pip install pandas openpyxl
# Optional (per feature)
pip install pyarrow # Parquet support
pip install xlrd # Legacy .xls read
pip install pandasql # SQL queries on DataFrames
pip install fastparquet # Alternative Parquet engine
Quick Routing
| Task | Script | Command |
|---|---|---|
| Inspect/profile data | data_wrangler.py |
inspect |
| Filter rows | data_wrangler.py |
filter --where "expr" |
| Sort by columns | data_wrangler.py |
sort --by Col --desc |
| Group & aggregate | data_wrangler.py |
group --by Col --agg "Col:func" |
| Merge/join files | data_wrangler.py |
merge f2 --on Key --how left |
| Pivot / unpivot | data_wrangler.py |
pivot --index/--unpivot |
| Remove duplicates | data_wrangler.py |
dedupe --subset "Col" |
| Fill missing values | data_wrangler.py |
fill --column Col --strategy mean |
| Drop cols/rows | data_wrangler.py |
drop --columns "A,B" |
| Rename columns | data_wrangler.py |
rename --map "old:new" |
| Cast types | data_wrangler.py |
cast --column Col --dtype datetime |
| Computed columns | data_wrangler.py |
derive --formula "New = A + B" |
| Random sample | data_wrangler.py |
sample --n 100 |
| Split by values | data_wrangler.py |
split --by Region |
| Validate rules | data_wrangler.py |
validate --rules rules.json |
| Apply formulas | data_wrangler.py |
formula --expr "C=A+B" |
| Convert formats | data_wrangler.py |
convert -o data.xlsx |
| SQL queries | data_wrangler.py |
query --sql "SELECT..." |
| List Excel sheets | excel_toolkit.py |
sheets |
| Extract sheet | excel_toolkit.py |
extract --sheet Sales -o sales.csv |
| Combine -> xlsx | excel_toolkit.py |
combine *.csv -o combined.xlsx |
| Format headers | excel_toolkit.py |
format --header-style bold,blue --autowidth |
| Freeze panes | excel_toolkit.py |
freeze --at B2 |
| Auto-filter | excel_toolkit.py |
autofilter |
| Dropdown validation | excel_toolkit.py |
validate --column Status --values "Open,Closed" |
| Protect sheet | excel_toolkit.py |
protect --password secret |
| Create workbook | excel_toolkit.py |
create --columns "Name,Age" -o template.xlsx |
What ships with it
3 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.
- 1mo ago First seen · 260 lines · 95 tokens per session scan A fb5eea504725
data-wrangler is a skill published in the GitHub repository georgekhananaev/claude-skills-vault (30 stars, last pushed 2mo ago), licensed MIT. It adds 95 tokens to every session and 2,792 once invoked, about $0.0004 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.
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