exploratory-data-analysis

exploratory-data-analysis is a skill for Claude Code, Codex from param087/agent-ml-skills. It costs 56 tokens per session (763 once invoked), scanned A, original, MIT.

A structured first review of a dataset before building a machine-learning model.

In plain words
What is it for?
Use it when a dataset is new, when you need to profile or summarize data, or when a model performs poorly and you need to understand its inputs.
Why use it?
It helps reveal the dataset's size, types, missing values, distributions, relationships, unusual values, and possible target leakage. Finding these issues early can prevent misleading or broken models.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when a dataset is new, when you need to profile or summarize data, or when a model performs poorly and you need to understand its inputs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/param087/agent-ml-skills/exploratory-data-analysis
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.

Any agent
npx skills add param087/agent-ml-skills --skill exploratory-data-analysis
Clone the repo
git clone --depth 1 https://github.com/param087/agent-ml-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for exploratory-data-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/param087/agent-ml-skills/exploratory-data-analysis/github.svg)](https://agentmods.dev/skills/param087/agent-ml-skills/exploratory-data-analysis)
Your own site
<a href="https://agentmods.dev/skills/param087/agent-ml-skills/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/param087/agent-ml-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.

agentmods 80×15 button for exploratory-data-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/param087/agent-ml-skills/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/param087/agent-ml-skills/exploratory-data-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 763 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original 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 5.1 $0.00056 $0.00763
Opus 5.5 $0.00022 $0.00305
Sonnet 5.5 $0.00011 $0.00153
Haiku 4.5 $0.00006 $0.00076

Measured 2mo ago against content hash f2fa14cfbcb7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 2mo 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.

skills/exploratory-data-analysis/SKILL.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Exploratory Data Analysis (EDA)

Overview

EDA is the disciplined first pass over a dataset: understand shape, types, distributions, missingness, relationships, and red flags before writing a single model. Skipping it is the #1 cause of silent modeling failures (leakage, broken splits, garbage features).

When to use

  • A new dataset just landed.
  • You're asked to "look at", "profile", "explore", or "summarize" data.
  • A model underperforms and you need to understand the inputs.

Workflow

Follow this order. Do not jump to modeling until every step is answered.

  1. Shape & types — rows, columns, dtypes, memory. Are numeric columns actually numeric?
  2. Missingness — per-column null counts and patterns (MCAR/MAR/MNAR). Is missingness itself predictive?
  3. Target analysis — distribution of the target (class balance / skew). This decides metrics and resampling.
  4. Univariate — distributions of each feature (histograms, value counts, describe()).
  5. Bivariate — feature vs target relationships; correlation matrix for numeric.
  6. Leakage scan — features too perfectly correlated with the target, IDs, timestamps, or post-outcome columns.
  7. Cardinality & outliers — high-cardinality categoricals, extreme values.

Reference snippet

import pandas as pd

df = pd.read_csv("data.csv")

# 1. Shape & types
print(df.shape)
print(df.dtypes.value_counts())
print(df.memory_usage(deep=True).sum() / 1e6, "MB")

# 2. Missingness
miss = df.isna().mean().sort_values(ascending=False)
print(miss[miss > 0])

# 3. Target (classification example)
print(df["target"].value_counts(normalize=True))

# 4-5. Numeric summary + correlations with target
num = df.select_dtypes("number")
print(num.describe().T)
print(num.corr()["target"].sort_values(ascending=False))

# 6. Leakage red flag: |corr| ~ 1.0 with target
corr_t = num.corr()["target"].drop("target").abs()
print("LEAKAGE SUSPECTS:", corr_t[corr_t > 0.95].index.tolist())

For a fast automated first look, ydata-profiling (ProfileReport(df)) or df.describe(include="all") is acceptable — but never let a tool replace the manual leakage scan.

Read the full file on GitHub · 71 lines

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. 2mo ago First seen · 71 lines · 56 tokens per session scan A f2fa14cfbcb7

Subscribe to this mod's changes

exploratory-data-analysis is a skill published in the GitHub repository param087/agent-ml-skills (13 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 763 once invoked, about $0.0002 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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