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Agent skill

Exploring Data

by oaustegard in oaustegard/claude-skills

Exploratory data analysis. An agent skill from oaustegard/claude-skills.

MITAuto-check passedData & Analytics

Install Exploring Data

skills CLI
$ npx skills add oaustegard/claude-skills --skill exploring-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install oaustegard/claude-skills exploring-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/oaustegard/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/exploring-data .claude/skills/exploring-data && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
exploring-data
GitHub stars
150
Token cost
~1.7k tokens
SKILL.md length
677 words
Files
13 (incl. scripts, references)
Skills in repo
69
Repo updated
First seen
Licence
MIT

At a glance

Exploratory data analysis. An agent skill from oaustegard/claude-skills.

  • Users upload .csv/.xlsx/.json/.parquet files
  • SKILL.md covers 0. Route by size FIRST, A. Standard path…, B. Large-file path (DuckDB,… and C. Sketch ops (any file size,…
  • Runs Python and Shell scripts from its folder; calls python3, bash and python
  • Request explore data

What it does

Exploring Data is an agent skill from oaustegard/claude-skills. Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row detection, cross-file key overlap ("can these join?"), dataset drift vs a stored baseline, and time-series profiling.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts and reference files (for example `CHANGELOG.md`, `README.md` and `references/USAGE.md`).

It sits in Data & Analytics, covering Forecasting and time series, Data analysis and DataFrames. It works with DuckDB and Microsoft Excel. The repository describes itself as: My collection of Claude skills. The licence is MIT.

When your agent uses it

  • Users upload .csv/.xlsx/.json/.parquet files
  • Request explore data
  • Analyze dataset

Example prompts

  • “explore data”
  • “analyze dataset”
  • “profile data”
  • “/exploring-data”

Requirements

  • Python 3
  • A Bash shell

What it can do on your machine

Read from SKILL.md and the folder at commit 6fc82b8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 8 files in scripts/ (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • bash
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Exploring Data loads about 1.7k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 677 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from oaustegard/claude-skills at commit 6fc82b8, republished under its MIT licence (© oaustegard). 677 words, ~1,664 tokens.

Download SKILL.mdSave it as .claude/skills/exploring-data/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
exploring-data
description
Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row detection, cross-file key overlap ("can these join?"), dataset drift vs a stored baseline, and time-series profiling.
metadata.version
0.2.0

Exploring Data

0. Route by size FIRST

bash
ls -la <filepath>   # or: wc -l for row estimate
  • < 200MB and < ~5M rows → ydata-profiling path (section A). Exact stats, interactive HTML.
  • Larger → large-file path (section B). ydata-profiling loads everything into pandas and will crawl or OOM; the DuckDB/sketch path runs in fixed memory at any size.
  • Task-specific ops (any size): duplicates, join feasibility, drift → section C.

A. Standard path (ydata-profiling)

1. Check if installed (instant)
bash
bash /mnt/skills/user/exploring-data/scripts/check_install.sh

Returns: installed or not_installed

2. Install if needed (one-time, ~19s)
bash
if [ "$(bash /mnt/skills/user/exploring-data/scripts/check_install.sh)" = "not_installed" ]; then
    bash /mnt/skills/user/exploring-data/scripts/install_ydata.sh
fi
3. Run analysis (always generates JSON + HTML by default)
bash
bash /mnt/skills/user/exploring-data/scripts/analyze.sh <filepath> [minimal|full] [html|json]

Defaults: minimal + html (also generates JSON)

Output:

  • eda_report.html - Interactive report for user
  • eda_report.json - Machine-readable for Claude analysis
4. If Claude needs to analyze (user asks "what do you think?" etc.)
bash
python /mnt/skills/user/exploring-data/scripts/summarize_insights.py /mnt/user-data/outputs/eda_report.json

Claude should read the stdout markdown summary, NOT the full JSON report.

5. Present findings visually (don't just hand over the ydata HTML)

The ydata report is exhaustive but dense; a link to it is a weak deliverable. Turn the JSON into a compact dashboard of the findings that matter:

bash
python3 /mnt/skills/user/exploring-data/scripts/visualize_findings.py \
    /mnt/user-data/outputs/eda_report.json
# → /mnt/user-data/outputs/eda_findings.html

Emits a single self-contained HTML file (Chart.js from cdnjs, dark-mode aware): missingness by column (tiered good/bad), the most skewed or zero-inflated numeric distributions as small-multiple histograms, and the largest categorical breakdowns. --top N caps charts per category (default 6). Also reads profile_large.py --json output, so the large-file path gets the same treatment.

Present BOTH files: eda_findings.html for the headline read, eda_report.html for the full drill-down. In a chat surface that renders inline visuals, prefer rendering the two or three findings that actually answer the user's question as inline charts over linking a file — a link the user has to open is the weakest form of "showing" data.

Modes

Minimal (default, 5-10s): overview, variable analysis, correlations, missing values, alerts Full (10-20s): minimal + scatter matrices, sample data, character analysis

Full-mode triggers: "comprehensive analysis", "detailed EDA", "full profiling", "deep analysis". Otherwise minimal.

Time series

If the data has a datetime index/column and the user cares about temporal behavior (gaps, trends, seasonality, autocorrelation), pass tsmode=True to ProfileReport — run the venv python directly instead of analyze.sh:

python
ProfileReport(df, tsmode=True, sortby="<datetime_col>", title=...)

This adds gap detection, stationarity and seasonality checks that the default report omits.

Small-file drift

Comparing two versions of a dataset that BOTH fit in memory: use ydata's native compare — ProfileReport(df_a).compare(ProfileReport(df_b)).to_file(...). For files too big to load, or comparing against a months-old file you no longer have, use the sketch snapshot/drift ops in section C.

B. Large-file path (DuckDB, fixed memory)

1. Install deps (idempotent, ~10s first time)
bash
bash /mnt/skills/user/exploring-data/scripts/install_large.sh
Show full SKILL.md (276 more words)Show less
2. Profile
bash
python3 /mnt/skills/user/exploring-data/scripts/profile_large.py <file> [--json out.json]

Streams the file through DuckDB: per-column null%, approximate distinct counts (HLL), min/max/mean, approximate quantiles (t-digest) for numerics, top-5 values for strings, plus quality flags (mostly-null, constant, id-like columns). Markdown lands on stdout — read it directly, no summarize step needed. Handles csv/tsv/parquet/json/ndjson. 1M rows profiles in seconds; memory is flat regardless of file size.

For ad-hoc follow-up queries on the same large file, use DuckDB SQL directly (duckdb.connect().execute("SELECT ... FROM read_csv_auto('...')")) rather than loading pandas.

C. Sketch ops (any file size, fixed memory)

All via scripts/sketch_ops.py (deps from install_large.sh). These answer questions profilers don't:

Near-duplicate rows
bash
python3 sketch_ops.py dups <file> [--threshold 0.9] [--cols a,b,c] [--unweighted]

Exact duplicates counted by hash; near-duplicates via MinHash LSH over row tokens. --threshold is a weighted Jaccard cutoff: a token occurring c times in a row counts c times, so new york new york and york new score 0.5 rather than 1.0. Pass --unweighted for set semantics, where repeats are discarded. Use --cols to restrict to the columns that define identity.

Key overlap / join feasibility
bash
python3 sketch_ops.py overlap <fileA> <fileB> --key <col> [--key-b <col>]

Theta sketches per key column → estimated intersection, Jaccard, and "% of A's keys in B" both ways — answers "will this join hold?" without loading either file.

Drift vs stored baseline
bash
python3 sketch_ops.py snapshot <file> --out baseline.sketch.json   # ~20KB
python3 sketch_ops.py drift <newfile> --baseline baseline.sketch.json

Snapshot serializes HLL (all columns) + KLL quantile sketches (numeric columns) to a small JSON. Drift reports schema changes, >10% shifts in distinct counts, and IQR-relative quantile movement. The snapshot is a few KB — store it (repo, memory) and diff next month's delivery against it without keeping the original file.

Note: snapshot/dups stream rows through Python (~1M rows in a few seconds); profile_large is pure DuckDB and faster. For a quick look at a big file, profile first, sketch ops only when the question calls for them.

© oaustegard, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (scripts, references) in exploring-data of oaustegard/claude-skills.

  • SKILL.md
  • CHANGELOG.md
  • README.md
  • references/USAGE.md
  • scripts/analyze.sh
  • scripts/check_install.sh
  • scripts/install_large.sh
  • scripts/install_ydata.sh
  • scripts/profile_large.py
  • scripts/sketch_ops.py
  • scripts/summarize_insights.py
  • scripts/visualize_findings.py
  • tests/test_sketch_ops.py

Open the folder on GitHubat commit 6fc82b8

Compare with similar skills

Exploring Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Exploring Data compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Exploring Data this skilloaustegard/claude-skills150—~1.7kAutomated safety check: PassMIT
Excel and CSV Data Analysisbytedance/deer-flow83k4 repos~2.2kAutomated safety check: PassMIT
Codebookbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~527Automated safety check: NotesCustom licence
Convert Fileduckdb/duckdb-skills6001 repos~720Automated safety check: NotesMIT
Raccoon DataanalysisSenseTime-Copilot/raccoon-dataanalysis-skill137—~1.9kAutomated safety check: PassNone
CSV Data Analysis5zjk5/prompt-engineering127—~2.6kAutomated safety check: PassNone

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Works with

Categories

Questions about Exploring Data

What does Exploring Data do?

Exploratory data analysis. An agent skill from oaustegard/claude-skills. Exploring Data is an agent skill from oaustegard/claude-skills. Exploratory data analysis.

When should I use Exploring Data?

Exploring Data fits situations like: users upload .csv/.xlsx/.json/.parquet files; request explore data; analyze dataset.

How do I install Exploring Data in Claude Code?

Run `npx skills add oaustegard/claude-skills --skill exploring-data -a claude-code`. Or copy the skill folder (exploring-data in oaustegard/claude-skills) into .claude/skills/exploring-data in your project. Claude Code loads it when a task matches its description.

How do I install Exploring Data in Codex?

Run `npx skills add oaustegard/claude-skills --skill exploring-data -a codex`. Or copy the skill folder (exploring-data in oaustegard/claude-skills) into .agents/skills/exploring-data in your project. Codex loads it when a task matches its description.

Can I use Exploring Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add oaustegard/claude-skills --skill exploring-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exploring-data, .gemini/skills/exploring-data, .github/skills/exploring-data and .opencode/skills/exploring-data in your project.

What does Exploring Data need to run?

Going by SKILL.md and its folder, Exploring Data needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python3, bash and python). Our summary lists: Python 3; A Bash shell.

Does Exploring Data access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Exploring Data safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Exploring Data use?

Exploring Data is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Exploring Data use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 460 tokens, read only when the agent opens those files.

What are the alternatives to Exploring Data?

Skills that share tags, products or a category with Exploring Data: Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars), Codebook (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Convert File (duckdb/duckdb-skills, 600 stars) and Raccoon Dataanalysis (SenseTime-Copilot/raccoon-dataanalysis-skill, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Exploring Data?

oaustegard (a GitHub user) maintains it in oaustegard/claude-skills, which has 150 GitHub stars. The repository holds 69 skills in this directory. The repository was last updated on October 8, 2026.

Source: oaustegard/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.