A data-science and machine-learning guide for exploring datasets, choosing models, creating useful inputs, applying statistics, visualizing results, and planning operations. Exploratory data analysis means systematically inspecting and summarizing data before modeling.

Profiling datasets, engineering model inputs, selecting models, running hypothesis tests, planning experiments, creating visualizations, and assessing machine-learning operations.

It helps choose an appropriate analysis or machine-learning approach and explains statistical concepts in the context of the data. It does not cover data pipelines or database design.

data-wizard — Skill by wyattowalsh

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/wyattowalsh/agents/data-wizard

Any agent

npx skills add wyattowalsh/agents --skill data-wizard

Clone the repo

git clone --depth 1 https://github.com/wyattowalsh/agents

Made for: Claude Code, Codex.

Or install agents, the plugin that ships this one along with the rest of its 16 skills, 20 agents, 5 hooks.

Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,275 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.

Model	Per session	Once invoked
Fable 5	$0.00049	$0.03275
Opus 5	$0.00024	$0.01638
Sonnet 5	$0.00010	$0.00655
Haiku 4.5	$0.00005	$0.00328

Measured 3d ago against content hash bd6fc1b87360, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-wizard 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 15 executable files (scripts/asset_toolkit/__init__.py, scripts/asset_toolkit/_shared.py, scripts/asset_toolkit/common.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/data-wizard/SKILL.md · 255 lines

How it starts

The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Wizard

Full-stack data science and ML engineering — from exploratory data analysis through model deployment strategy. Adapts approach based on complexity classification.

Canonical Vocabulary

Term	Definition
EDA	Exploratory Data Analysis — systematic profiling and summarization of a dataset
feature	An individual measurable property used as input to a model
feature engineering	Creating, transforming, or selecting features to improve model performance
hypothesis test	A statistical procedure to determine if observed data supports a claim
p-value	Probability of observing data at least as extreme as the actual results, assuming the null hypothesis is true
effect size	Magnitude of a difference or relationship, independent of sample size
power analysis	Determining sample size needed to detect an effect of a given size
CUPED	Controlled-experiment Using Pre-Experiment Data — variance reduction technique for A/B tests
MLOps maturity	Level 0 (manual), Level 1 (ML pipeline), Level 2 (CI/CD + CT), Level 3 (full automation)
data quality score	Composite metric across completeness, consistency, accuracy, timeliness, uniqueness
profile	Statistical summary of a dataset: types, distributions, missing patterns, correlations
anomaly	Data point or pattern deviating significantly from expected behavior

Dispatch

`$ARGUMENTS`	Action
`eda <data>`	EDA — profile dataset, summary stats, missing patterns, distributions
`model <task>`	Model Selection — recommend models, libraries, training plan for task
`features <data>`	Feature Engineering — suggest transformations, encoding, selection pipeline
`stats <question>`	Stats — select and design statistical hypothesis test
`viz <data>`	Visualization — recommend chart types, encodings, layout for data
`viz plan <data> [goal]`	Viz Plan — JSON chart plan from data + goal via `viz-planner.py`
`viz render <plan> <data>`	Viz Render — PNG/HTML charts from plan via `viz-renderer.py`
`viz dashboard <profile>`	Viz Dashboard — HTML EDA dashboard via `dashboard-builder.py`
`experiment <hypothesis>`	Experiment Design — A/B test design, power analysis, CUPED

Read the full file on GitHub · 255 lines

Files

What ships with it

39 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.

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.

3d ago First seen · 255 lines · 49 tokens per session scan A bd6fc1b87360

Subscribe to this mod's changes

data-wizard is a skill published in the GitHub repository wyattowalsh/agents (5 stars, last pushed 12d ago), licensed MIT. It adds 49 tokens to every session and 3,275 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.

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