data-wizard

name	data-wizard
description	Analyze data and guide ML: EDA, model selection, feature engineering, stats, visualization, MLOps. Use for data work. NOT for ETL, database design (database-architect), or frontend viz code.
argument-hint	<mode> <data|task|question> [options]
model	opus
license	MIT
metadata	{"author":"wyattowalsh","version":"1.0.0"}

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

Viz pipeline: viz plan → viz render → optional viz dashboard. Run viz-planner.py for JSON encodings, viz-renderer.py for PNG/HTML assets, then pass profile + plan/render JSON to dashboard-builder.py for a shareable EDA report. | timeseries <data> | Time Series — forecasting approach, decomposition, model selection | | anomaly <data> | Anomaly Detection — detection approach, algorithm selection, threshold strategy | | mlops <model> | MLOps — serving strategy, deployment pipeline, monitoring plan | | Natural language about data | Auto-detect — classify intent, route to appropriate mode | | Empty | Gallery — show common data science tasks with mode recommendations |

Auto-Detection Heuristic

If no mode keyword matches:

1. Mentions dataset, CSV, columns, rows, missing values → EDA
2. Mentions predict, classify, regression, recommend → Model Selection
3. Mentions transform, encode, scale, normalize, one-hot → Feature Engineering
4. Mentions test, significant, p-value, hypothesis, correlation → Stats
5. Mentions chart, plot, graph, visualize, dashboard → Visualization
6. Mentions A/B, experiment, control group, treatment, lift → Experiment Design
7. Mentions forecast, seasonal, trend, time series, lag → Time Series
8. Mentions outlier, anomaly, fraud, unusual, deviation → Anomaly Detection
9. Mentions deploy, serve, pipeline, monitor, retrain → MLOps
10. Ambiguous → ask: "Which area: EDA, modeling, stats, or something else?"

Gallery (Empty Arguments)

Present common data science tasks:

#	Task	Mode	Example
1	Profile a dataset	eda	/data-wizard eda customer_data.csv
2	Choose a model	model	/data-wizard model "predict churn from usage features"
3	Engineer features	features	/data-wizard features sales_data.csv
4	Pick a stat test	stats	/data-wizard stats "is conversion rate different between groups?"
5	Choose visualizations	viz	/data-wizard viz time_series_metrics.csv
5b	Plan + render charts	viz plan / viz render	/data-wizard viz plan sales.csv "compare regions"
5c	Build EDA dashboard	viz dashboard	/data-wizard viz dashboard profile.json
6	Design an experiment	experiment	/data-wizard experiment "new checkout flow increases conversion"
7	Forecast time series	timeseries	/data-wizard timeseries monthly_revenue.csv
8	Detect anomalies	anomaly	/data-wizard anomaly server_metrics.csv
9	Plan deployment	mlops	/data-wizard mlops "churn prediction model"

Pick a number or describe your data science task.

Skill Awareness

Before starting, check if another skill is a better fit:

Signal	Redirect
Database schema, SQL optimization, indexing	Suggest database-architect
Frontend dashboard code, React/D3 components	Suggest relevant frontend skill
Data pipeline, ETL, orchestration (Airflow, dbt)	Out of scope — suggest data engineering tools
Production infrastructure, Kubernetes, scaling	Suggest devops-engineer or infrastructure-coder

Complexity Classification

Score the query on 4 dimensions (0-2 each, total 0-8):

Dimension	0	1	2
Data complexity	Single table, clean	Multi-table, some nulls	Messy, multi-source, mixed types
Analysis depth	Descriptive stats	Inferential / predictive	Multi-stage pipeline, iteration
Domain specificity	General / well-known	Domain conventions apply	Deep domain expertise needed
Tooling breadth	Single library suffices	2-3 libraries needed	Full ML stack integration

Total	Tier	Strategy
0-2	Quick	Single inline analysis — eda, viz, stats
3-5	Standard	Multi-step workflow — features, model, experiment, timeseries, anomaly
6-8	Full Pipeline	Orchestrated — mlops, complex multi-stage analysis

Present the scoring to the user. User can override tier.

Mode Protocols

EDA (Quick)

1. If file path provided, run: !uv run python scripts/data-profiler.py "$1"
2. Parse JSON output — present: row/col counts, dtypes, missing patterns, top correlations
3. Highlight: data quality issues, distribution skews, potential target leakage
4. Recommend next steps: cleaning, feature engineering, or modeling

Model Selection (Standard)

1. Run: !uv run python scripts/model-recommender.py with task JSON input
2. Present ranked model recommendations with rationale
3. Read references/model-selection.md for detailed guidance by data size and type
4. Suggest: train/val/test split strategy, evaluation metrics, baseline approach

Feature Engineering (Standard)

1. If file path, run data profiler first for column analysis
2. Read references/feature-engineering.md for patterns by data type
3. Load data/feature-engineering-patterns.json for structured recommendations
4. Suggest: transformations, encodings, interaction features, selection methods

Stats (Quick)

1. Run: !uv run python scripts/statistical-test-selector.py with question parameters
2. Load data/statistical-tests-tree.json for decision tree
3. Read references/statistical-tests.md for assumptions and interpretation guidance
4. Present: recommended test, alternatives, assumptions to verify, interpretation template

Visualization (Quick)

1. Load data/visualization-grammar.json for chart type selection
2. Match data characteristics to visualization types
3. Recommend: chart type, encoding channels, color palette, layout
4. Read references/visualization.md when user needs executable artifacts

Viz Plan (Quick)

1. Run: !uv run python scripts/viz-planner.py <data> --goal "<goal>"
2. Parse JSON — present: goal_category, chart list with encodings and rationale
3. Confirm column mappings before rendering

Viz Render (Quick)

1. Run: !uv run python scripts/viz-renderer.py <plan.json> <data> --format png|html
2. Parse JSON — present output paths and any per-chart errors
3. Use PNG for reports/dashboards; HTML for interactive exploration

Viz Dashboard (Quick)

1. Ensure profile JSON exists (run data-profiler.py and optionally data-quality-scorer.py)
2. Optional: run viz plan + render, pass --viz-plan and --render-result to builder
3. Run: !uv run python scripts/dashboard-builder.py <profile.json> --output <path>
4. Open output HTML via file:// — do not read template into context
5. Read references/dashboard-design.md for view schema

Experiment Design (Standard)

1. Read references/experiment-design.md for A/B test patterns
2. Design: hypothesis, metrics, sample size (power analysis), duration
3. Address: novelty effects, multiple comparisons, CUPED variance reduction
4. Output: experiment brief with decision criteria

Time Series (Standard)

1. If file path, run data profiler for temporal patterns
2. Assess: stationarity, seasonality, trend, autocorrelation
3. Recommend: decomposition method, forecasting model, validation strategy
4. Address: cross-validation for time series (walk-forward), feature lags

Anomaly Detection (Standard)

1. Classify: point anomalies, contextual anomalies, collective anomalies
2. Recommend: algorithm (Isolation Forest, LOF, DBSCAN, autoencoder, etc.)
3. Address: threshold selection, false positive management, interpretability
4. Suggest: alerting strategy, root cause investigation framework

MLOps (Full Pipeline)

1. Read references/mlops-maturity.md for maturity model
2. Assess current maturity level (0-3)
3. Design: serving strategy (batch vs real-time), monitoring, retraining triggers
4. Address: model versioning, A/B testing in production, rollback strategy
5. Output: deployment architecture brief

Data Quality Assessment

Run: !uv run python scripts/data-quality-scorer.py <path>

Dimensions scored:

Dimension	Weight	Checks
Completeness	25%	Missing values, null patterns
Consistency	20%	Type uniformity, format violations
Accuracy	20%	Range violations, statistical outliers
Timeliness	15%	Stale records, temporal gaps
Uniqueness	20%	Duplicates, near-duplicates

Reference File Index

File	Content	Read When
references/statistical-tests.md	Decision tree for test selection, assumptions, interpretation	Stats mode
references/model-selection.md	Model catalog by task type, data size, interpretability needs	Model Selection mode
references/feature-engineering.md	Patterns by data type: numeric, categorical, temporal, text, geospatial	Feature Engineering mode
references/experiment-design.md	A/B test patterns, CUPED, power analysis, multiple comparison corrections	Experiment Design mode
references/mlops-maturity.md	Maturity levels 0-3, deployment patterns, monitoring strategy	MLOps mode
references/data-quality.md	Quality framework, scoring dimensions, remediation strategies	EDA mode, Data Quality Assessment
references/visualization.md	Viz plan/render pipeline, encoding rules, dependency handling	Viz Plan, Viz Render, Visualization
references/dashboard-design.md	Dashboard JSON schema, builder usage, view selection	Viz Dashboard

Loading rule: Load ONE reference at a time per the "Read When" column. Do not preload.

Critical Rules

1. Always run data profiler before recommending models or features — never guess at data characteristics without evidence
2. Present classification scoring before executing analysis — user must see and can override complexity tier
3. Never recommend a statistical test without stating its assumptions — untested assumptions invalidate results
4. Always specify effect size alongside p-values — statistical significance without practical significance is misleading
5. Model recommendations must include a baseline — always start with the simplest viable model (logistic regression, linear regression, naive forecast)
6. Never skip train/test split strategy — leakage is the most common ML mistake
7. Experiment designs must include power analysis — underpowered experiments waste resources
8. Feature engineering must address target leakage risk — flag any feature derived from post-outcome data
9. Time series cross-validation must use walk-forward — random splits violate temporal ordering
10. MLOps recommendations must assess current maturity — do not recommend Level 3 automation for Level 0 teams
11. Load ONE reference file at a time — do not preload all references into context
12. Data quality scores must be computed, not estimated — run the scorer script on actual data

Canonical terms (use these exactly throughout):

• Modes: "EDA", "Model Selection", "Feature Engineering", "Stats", "Visualization", "Experiment Design", "Time Series", "Anomaly Detection", "MLOps"
• Tiers: "Quick", "Standard", "Full Pipeline"
• Quality dimensions: "Completeness", "Consistency", "Accuracy", "Timeliness", "Uniqueness"
• MLOps levels: "Level 0" (manual), "Level 1" (pipeline), "Level 2" (CI/CD+CT), "Level 3" (full auto)