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 oaustegard/claude-skills --skill exploring-datagit clone --depth 1 https://github.com/oaustegard/claude-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/oaustegard/claude-skills/exploring-data)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/exploring-data"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/exploring-data.svg" alt="Measured on agentmods" 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 | $0.00108 | $0.01699 |
| Opus 5 | $0.00054 | $0.00849 |
| Sonnet 5 | $0.00022 | $0.00340 |
| Haiku 4.5 | $0.00011 | $0.00170 |
Grade A, and why
exploring-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploring Data
0. Route by size FIRST
ls -la <filepath> # or: wc -l for row estimate
- < 200MB and < ~5M rows → ydata-profiling path (section A). Exact stats, interactive HTML.
- Larger → large-file path (section B). ydata-profiling loads everything into pandas and will crawl or OOM; the DuckDB/sketch path runs in fixed memory at any size.
- Task-specific ops (any size): duplicates, join feasibility, drift → section C.
A. Standard path (ydata-profiling)
1. Check if installed (instant)
bash /mnt/skills/user/exploring-data/scripts/check_install.sh
Returns: installed or not_installed
2. Install if needed (one-time, ~19s)
if [ "$(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 /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 usereda_report.json- Machine-readable for Claude analysis
4. If Claude needs to analyze (user asks "what do you think?" etc.)
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:
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.
What ships with it
12 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.
- CHANGELOG.md 2.7 KB
- README.md 300 B
- references/USAGE.md 1.8 KB
- scripts/analyze.sh 2.2 KB runs code
- scripts/check_install.sh 184 B runs code
- scripts/install_large.sh 335 B runs code
- scripts/install_ydata.sh 717 B runs code
- scripts/profile_large.py 4.2 KB runs code
- scripts/sketch_ops.py 8.1 KB runs code
- scripts/summarize_insights.py 12 KB runs code
- scripts/visualize_findings.py 12 KB runs code
- tests/test_sketch_ops.py 3.0 KB runs code
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.
- today Changed · +3 lines c62d17bfe532
- 8d ago First seen · 154 lines · 108 tokens per session scan A ba40a554488f
exploring-data is a skill published in the GitHub repository oaustegard/claude-skills (147 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 1,699 once invoked, about $0.0005 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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