Data Wizard

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

Dispatch

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:

Pick a number or describe your data science task.

Skill Awareness

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

Complexity Classification

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

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:

Reference File Index

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)