data-exploration

data-exploration is a skill for Claude Code, Codex from zpower426/datapowers. It costs 33 tokens per session (2,277 once invoked), scanned A, original, MIT.

A skill for exploratory data analysis, the first examination of a dataset before modeling or changing it. It checks the data's structure, quality, distributions, time patterns, relationships, and possible target leakage.

In plain words
What is it for?
Use it when opening a dataset for the first time or when asked to perform EDA. It helps inspect columns, missing values, outliers, categories, dates, correlations, and target variables.
Why use it?
It prevents models from being built on misunderstood, incomplete, biased, or incorrectly prepared data. It also produces a structured summary of findings, next steps, and blockers.

Skill for Claude CodeCodex

Part of the datapowers plugin — 20 skills, 3 commands, 3 agents, 1 hook 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/zpower426/datapowers/data-exploration
Any agent
npx skills add zpower426/datapowers --skill data-exploration
Clone the repo
git clone --depth 1 https://github.com/zpower426/datapowers

Made for: Claude Code, Codex.

Or install datapowers, the plugin that ships this one along with the rest of its 20 skills, 3 commands, 3 agents, 1 hook.

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 data-exploration

README.md
[![agentmods](https://agentmods.dev/badge/skills/zpower426/datapowers/data-exploration.svg)](https://agentmods.dev/skills/zpower426/datapowers/data-exploration)
Your own site
<a href="https://agentmods.dev/skills/zpower426/datapowers/data-exploration"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/data-exploration.svg" alt="Measured on agentmods" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,277 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00033 $0.02277
Opus 5 $0.00016 $0.01138
Sonnet 5 $0.00007 $0.00455
Haiku 4.5 $0.00003 $0.00228

Measured 5d ago against content hash e672a4d8195f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-exploration 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 5d 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/data-exploration/SKILL.md · 267 lines

How it starts

The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Exploration (EDA)

Systematic, evidence-based exploration of a dataset. Produces a structured understanding of data shape, quality, distributions, and relationships.

Iron Law: NO MODELING WITHOUT EXPLORATORY DATA ANALYSIS FIRST

Checklist

You MUST create a task for each of these items and complete them in order:

  1. Dataset overview — shape, schema, dtypes, memory usage
  2. Target variable analysis — distribution, class balance (if classification)
  3. Missing data audit — count, pattern, mechanism (MCAR/MAR/MNAR)
  4. Numeric feature analysis — distributions, outliers, skewness
  5. Categorical feature analysis — cardinality, value distributions, rare categories
  6. Temporal analysis — if datetime columns exist, check trends and gaps
  7. Relationship analysis — correlations, mutual information with target
  8. Leakage candidate screening — flag suspicious features
  9. Data quality score — per-column quality rating
  10. EDA summary — key findings, recommended next steps, blockers

Process Flow

digraph eda {
    "Dataset overview" [shape=box];
    "Has target variable?" [shape=diamond];
    "Target variable analysis" [shape=box];
    "Missing data audit" [shape=box];
    "Numeric analysis" [shape=box];
    "Categorical analysis" [shape=box];
    "Has datetime columns?" [shape=diamond];
    "Temporal analysis" [shape=box];
    "Relationship analysis" [shape=box];
    "Leakage screening" [shape=box];
    "Quality score" [shape=box];
    "EDA summary + save report" [shape=doublecircle];

    "Dataset overview" -> "Has target variable?";
    "Has target variable?" -> "Target variable analysis" [label="yes"];
    "Has target variable?" -> "Missing data audit" [label="no"];
    "Target variable analysis" -> "Missing data audit";
    "Missing data audit" -> "Numeric analysis";
    "Numeric analysis" -> "Categorical analysis";
    "Categorical analysis" -> "Has datetime columns?";
    "Has datetime columns?" -> "Temporal analysis" [label="yes"];
    "Has datetime columns?" -> "Relationship analysis" [label="no"];
    "Temporal analysis" -> "Relationship analysis";
    "Relationship analysis" -> "Leakage screening";
    "Leakage screening" -> "Quality score";
    "Quality score" -> "EDA summary + save report";
}

Read the full file on GitHub · 267 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. 5d ago First seen · 267 lines · 33 tokens per session scan A e672a4d8195f

Subscribe to this mod's changes

data-exploration is a skill published in the GitHub repository zpower426/datapowers (1 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 2,277 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 2026-08-31.

Related

Other skills, from other repositories

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

fba-simulator

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

aiming-lab/AutoResearchClaw · 69 tokens

flux-analyzer

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

aiming-lab/AutoResearchClaw · 44 tokens

gsmm-validator

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

aiming-lab/AutoResearchClaw · 52 tokens

gsmm-builder

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

aiming-lab/AutoResearchClaw · 45 tokens

stat-result-validator

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

aiming-lab/AutoResearchClaw · 36 tokens