$ARGUMENTS

eda <data>

model <task>

features <data>

stats <question>

viz <data>

viz plan <data> [goal]

viz-planner.py

viz render <plan> <data>

viz-renderer.py

viz dashboard <profile>

dashboard-builder.py

experiment <hypothesis>

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 |

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?"

eda

/data-wizard eda customer_data.csv

model

/data-wizard model "predict churn from usage features"

features

/data-wizard features sales_data.csv

stats

/data-wizard stats "is conversion rate different between groups?"

viz

/data-wizard viz time_series_metrics.csv

viz plan

viz render

/data-wizard viz plan sales.csv "compare regions"

viz dashboard

/data-wizard viz dashboard profile.json

experiment

/data-wizard experiment "new checkout flow increases conversion"

timeseries

/data-wizard timeseries monthly_revenue.csv

anomaly

/data-wizard anomaly server_metrics.csv

mlops

/data-wizard mlops "churn prediction model"

Pick a number or describe your data science task.

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

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

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

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

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

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

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

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

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

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

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

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

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

• 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)