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 bestdeejay-design/agent-skills --skill data-analysisgit clone --depth 1 https://github.com/bestdeejay-design/agent-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/bestdeejay-design/agent-skills/data-analysis)<a href="https://agentmods.dev/skills/bestdeejay-design/agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/data-analysis/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/bestdeejay-design/agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/data-analysis.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00157 | $0.01612 |
| Opus 5.5 | $0.00063 | $0.00645 |
| Sonnet 5.5 | $0.00031 | $0.00322 |
| Haiku 4.5 | $0.00016 | $0.00161 |
Grade A, and why
data-analysis 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
Профилирование датасета: статистика полей, корреляции, аномалии и рекомендации.
Загружай этот скилл когда нужно разобраться в данных: понять типы полей, распределения, пропуски, корреляции между числовыми полями и что стоит почистить перед анализом или моделированием.
🎯 When to use
Use this skill when:
- Есть CSV или JSON-массив объектов и нужен профиль датасета (EDA)
- Нужно понять типы полей, пропуски, распределения и выбросы
- Просят «проанализировать данные», «почитать данные», «сделать EDA»
- Нужны корреляции между числовыми полями и рекомендации по очистке данных
Do NOT use when:
- Нужен SQL-запрос к базе данных — это
sql-helper - Нужна обработка CSV как таблицы (фильтры, join, преобразования) — это
csv-pro - Нужны графики/визуализации — скрипт выдаёт текстовый отчёт (markdown/JSON), не картинки
📦 Files
SKILL.md— этот файлscripts/data_analyze.py— профилировщик датасета (Python 3 stdlib)references/canonical-patterns.md— эталонные EDA-паттерны канонических инструментов
Canonical analogues
Эталонные EDA-паттерны и gap-анализ скрипта против канонических инструментов —
в references/canonical-patterns.md. Топ-аналоги:
- ydata-profiling — эталонная структура отчёта (Overview/Alerts/Variables/Correlations/Missing/Sample) и алерты качества с порогами.
- sweetviz — целевой анализ (target_feat) и mixed-type ассоциации (uncertainty coefficient, correlation ratio).
- DuckDB SUMMARIZE — профилирование одной SQL-командой: min/max/approx_unique/avg/std/q25/q50/q75/null_percentage.
- D-Tale — интерактивный GUI: Describe, Outlier Detection, Duplicates, Missing Analysis, Predictive Power Score.
- skimr / DataExplorer (R) — консольное профилирование по типам и метрики introduce() (memory_usage, complete_rows).
🧰 Usage
# CSV → markdown-отчёт (по умолчанию):
python3 skills/data-analysis/scripts/data_analyze.py --input data.csv
# JSON-массив объектов → markdown-отчёт:
python3 skills/data-analysis/scripts/data_analyze.py --input data.json
# JSON-отчёт (для программного использования):
python3 skills/data-analysis/scripts/data_analyze.py --input data.csv --output json
# HTML-отчёт с графиками:
python3 skills/data-analysis/scripts/data_analyze.py --input data.csv --output html
# Топ-5 значений вместо 10 и свой заголовок:
python3 skills/data-analysis/scripts/data_analyze.py --input data.csv --top 5 --title "Продажи 2026"
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 Changed · +8 lines f025fa6947fa
- 1mo ago Changed · +31 lines · +4 tokens per session 77dbd44e859e
- 1mo ago First seen · 75 lines · 153 tokens per session scan A a5b299fb505a
data-analysis is a skill published in the GitHub repository bestdeejay-design/agent-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 157 tokens to every session and 1,612 once invoked, about $0.0006 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 2026-08-31.
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python-release
Handle Python SDK release, build, bump, packaging metadata, PyPI client pin, uv.lock, nox/build workflow, and publish verification changes. Use for Python release process work or dependency pin bumps; do not use for ordinary Python feature implementation.