exploratory-data-analysis

A guide for identifying unfamiliar scientific file formats and examining their contents. It covers more than 200 formats, including files that are not simple tables.

In plain words
What is it for?
Use it to recognize scientific data files, choose suitable ways to inspect them, and perform exploratory data analysis (EDA), the first review of a dataset’s structure and patterns.
Why use it?
It helps when a dataset cannot be understood with ordinary table-based tools. You get format-specific guidance instead of guessing how to inspect the files.

Skill for Claude CodeCodex

Part of the ds plugin — 19 skills, 8 commands shipped together

Install

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.

agentmods
npx agentmods add skills/andikarachman/data-science-plugin/exploratory-data-analysis
Any agent
npx skills add andikarachman/data-science-plugin --skill exploratory-data-analysis
Clone the repo
git clone --depth 1 https://github.com/andikarachman/data-science-plugin

Made for: Claude Code, Codex.

Or install ds, the plugin that ships this one along with the rest of its 19 skills, 8 commands.

Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,470 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00047 $0.03470
Opus 5 $0.00023 $0.01735
Sonnet 5 $0.00009 $0.00694
Haiku 4.5 $0.00005 $0.00347

Measured 3d ago against content hash 8c0f8a791bfa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/eda_analyzer.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/exploratory-data-analysis/SKILL.md · 450 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 3d ago First seen · 450 lines · 47 tokens per session scan A 8c0f8a791bfa

Subscribe to this mod's changes

exploratory-data-analysis is a skill published in the GitHub repository andikarachman/data-science-plugin (14 stars, last pushed 6mo ago), with no licence file. It adds 47 tokens to every session and 3,470 once invoked, about $0.0002 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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