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 h4vzz/awesome-ai-agent-skills --skill exploratory-data-analysisgit clone --depth 1 https://github.com/h4vzz/awesome-ai-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/h4vzz/awesome-ai-agent-skills/exploratory-data-analysis)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/exploratory-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/h4vzz/awesome-ai-agent-skills/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/exploratory-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 | $0.00025 | $0.01899 |
| Opus 5.5 | $0.00010 | $0.00760 |
| Sonnet 5.5 | $0.00005 | $0.00380 |
| Haiku 4.5 | $0.00003 | $0.00190 |
Grade A, and why
exploratory-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.
This is a copy
92% identical to exploratory-data-analysis — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploratory Data Analysis
This skill enables an AI agent to perform structured exploratory data analysis (EDA) on any tabular dataset. The agent systematically profiles the data's shape and types, examines distributions, computes correlations, detects outliers, and produces a summary of findings. EDA is the critical first step before any modeling or reporting — it reveals what the data actually contains versus what it is assumed to contain.
Workflow
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Load and inspect basic structure. Read the dataset and immediately report its shape (rows, columns), column names, data types, and memory footprint. Display the first 5 and last 5 rows to catch header issues, trailing garbage rows, or encoding artifacts. This takes under a second but prevents hours of downstream confusion.
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Assess data quality. Count nulls per column as both absolute and percentage. Identify columns with zero variance (constant values), high cardinality categoricals (e.g., a "notes" field with unique values per row), and mixed-type columns. Build a concise quality scorecard: columns with >5% missing, columns with suspicious types, and duplicate row counts.
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Analyze distributions of individual variables. For numeric columns, compute mean, median, standard deviation, skewness, and kurtosis. Plot histograms or KDE plots. For categorical columns, show value counts and proportions for the top 10 categories. Flag highly imbalanced distributions (e.g., a binary target where one class is under 5%).
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Explore relationships between variables. Compute the full correlation matrix for numeric columns and visualize it as a heatmap. For categorical-vs-numeric relationships, use grouped box plots or violin plots. For categorical-vs-categorical, use contingency tables or mosaic plots. Highlight pairs with correlation above 0.7 or below -0.7.
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Detect outliers and anomalies. Apply the IQR method to every numeric column and report the count and percentage of outlier values. Visualize outliers with box plots. Cross-reference outliers across columns — a row that is an outlier in multiple columns simultaneously often represents a data entry error or a genuinely unusual observation.
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 · 153 lines · 25 tokens per session scan A 62b322833238
exploratory-data-analysis is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 25 tokens to every session and 1,899 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to exploratory-data-analysis, differing in 2 lines, and is treated as a copy.
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