"Routes AutoViz users to automated EDA, data-quality, and
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill autoviz --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: autoviz
description: "Routes AutoViz users to automated EDA, data-quality, and
text/wordcloud workflows."
metadata:
disco-role: operating
disable-model-invocation: true
license: Apache 2.0
---
# AutoViz
Use this skill when a user wants to explore a tabular dataset with AutoViz, fix data-quality problems with `FixDQ`, or generate wordclouds from string columns. Keep this skill router-like: send the user to the most specific sub-skill, then use the bundled references and scripts for details.
## First decision
- If the task is about loading a file or DataFrame and producing plots, use [`sub-skills/eda-visualization/SKILL.md`](sub-skills/eda-visualization/SKILL.md).
- If the task is about data-quality reporting or `FixDQ`, use [`sub-skills/data-quality-fixes/SKILL.md`](sub-skills/data-quality-fixes/SKILL.md).
- If the task is about text columns, NLP cleanup, or wordclouds, use [`sub-skills/text-wordclouds/SKILL.md`](sub-skills/text-wordclouds/SKILL.md).
## Minimal usage shape
```python
from autoviz import AutoViz_Class
AV = AutoViz_Class()
dft = AV.AutoViz(
filename,
sep=",",
depVar="",
dfte=df,
header=0,
verbose=1,
lowess=False,
chart_format="svg",
max_rows_analyzed=150000,
max_cols_analyzed=30,
save_plot_dir=None,
)
```
## What to read next
- Read [`references/overview.md`](references/overview.md) for the package surface and the quickest route to the right sub-skill.
- Read [`references/install-and-compatibility.md`](references/install-and-compatibility.md) if imports, `pip check`, `xgboost`, pandas, or interactive-backend dependencies fail.
- Read [`references/chart-formats.md`](references/chart-formats.md) when the user asks for `png`, `svg`, `jpg`, `bokeh`, `server`, or `html` output behavior.
- Read [`references/troubleshooting.md`](references/troubleshooting.md) for import, environment, plotting, and text-column failure modes.
- Read [`references/api-reference.md`](references/api-reference.md) when you need exact signatures or helper names.
- Read [`references/repo-provenance.md`](references/repo-provenance.md) when you need to check source freshness or decide whether the skill should be refreshed.
- Read [`references/repo-routing-metadata.json`](references/repo-routing-metadata.json) when you need the managed router placement metadata used during import.
- Read [`scripts/inspect_install.py`](scripts/inspect_install.py) when you need a quick runtime snapshot from a prepared environment.
## Common routing cues
- `AutoViz_Class`, `AutoViz_Main`, `filename`, `dfte`, `chart_format`, `save_plot_dir`, `max_rows_analyzed`, or `max_cols_analyzed` usually mean the EDA sub-skill.
- `FixDQ`, `data_cleaning_suggestions`, duplicate rows, infinity, rare categories, mixed types, or leakage usually mean the data-quality sub-skill.
- `wordcloud`, `nltk`, `textblob`, `emoji`, `stopwords`, `discrete string`, or NLP columns usually mean the text sub-skill.
- If the user asks about installation, package versions, or why `import autoviz` fails, route to the relevant references first and then return to the right sub-skill.
## Import note
`import autoviz` prints a banner in this repository version. That is expected and not a failure.
## Quick expectations
- The package is CPU-oriented.
- Interactive chart formats depend on `hvplot`, `holoviews`, `panel`, `bokeh`, and `IPython`.
- `data_cleaning_suggestions` uses `pandas_dq`.
- `FixDQ` is the transformer-style path for reusable data-quality cleanup.
- Wordcloud generation may trigger an NLTK download if string columns are present.
- A tiny toy DataFrame can be classified in surprising ways, so use the bundled smoke scripts when debugging.
## What not to do
- Do not point users at the original source checkout as a required runtime dependency.
- Do not suggest source notebooks or repo tests as the primary user workflow when a bundled script exists.
- Do not claim accelerator requirements; this skill's covered workflows are CPU-based.
- Do not hide compatibility warnings in prose when `pip check` or import output shows a concrete failure.
## Handoff shape
When the user is still deciding, keep the response short and route them to the right sub-skill plus the correct reference.
When the user wants action, use the bundled script for that sub-skill, verify the runtime, and then summarize the result in package terms rather than source-file terms.
## Escalation
If the user wants a broader comparison with another package, first finish the AutoViz route and then explain the boundary clearly.
If the issue is really a repository install or compatibility failure, point them to the install-and-compatibility and troubleshooting references before changing the workflow.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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