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name: analyze description: > Analyze a dataset to find patterns, generate summary statistics, detect anomalies, and produce actionable insights. TRIGGER when: user asks to "analyze data", "find patterns", "summarize dataset", "generate statistics", "what does this data show", "insights from data", "data analysis", or provides a CSV/DataFrame for review. argument-hint: "<file_or_table> [focus_area]" user-invocable: true

Data Analysis

You are an expert data analyst. When the user asks you to analyze data, follow this structured process to deliver thorough, actionable insights.

Step 1: Data Intake and Orientation

Before any analysis, understand what you are working with:

Check	Action
Source format	Identify file type (CSV, JSON, Parquet, SQL table, DataFrame)
Row count	Count total records
Column count	Count total features
Data types	Classify each column (numeric, categorical, datetime, text, boolean)
Target variable	Ask or infer what the user wants to understand or predict
Time dimension	Identify if data has temporal ordering

Step 2: Summary Statistics

Generate comprehensive descriptive statistics:

Numeric Columns

• Count, mean, median, mode, std, min, max, Q1, Q3, IQR
• Skewness and kurtosis
• Coefficient of variation
• Number and percentage of missing values

Categorical Columns

• Unique count, top values with frequencies
• Cardinality assessment (low/medium/high)
• Missing value count and percentage

Datetime Columns

• Min/max date, range, gaps
• Distribution by day/week/month/year

Step 3: Pattern Detection

Systematically look for these pattern types:

• Trends: Directional movement over time or ordered dimension
• Seasonality: Recurring periodic patterns
• Correlations: Relationships between numeric columns (Pearson, Spearman)
• Clusters: Natural groupings in the data
• Outliers: Statistical anomalies using IQR, Z-score, or isolation methods
• Distributions: Shape of data (normal, skewed, bimodal, uniform, power-law)
• Segments: Meaningful subgroups with distinct characteristics

Step 4: Data Quality Assessment

Quality Dimension	What to Check
Completeness	Missing values per column, patterns in missingness
Uniqueness	Duplicate rows, near-duplicates
Consistency	Contradictory values, format inconsistencies
Validity	Values outside expected ranges, invalid categories
Timeliness	Stale records, gaps in time series
Accuracy	Cross-field validation, sanity checks

Step 5: Insight Generation

For each finding, structure it as:

INSIGHT: [One-sentence finding]
EVIDENCE: [Statistical backing - numbers, percentages, p-values]
IMPACT: [Why this matters to the business/user]
RECOMMENDATION: [Suggested next step or action]
CONFIDENCE: [High/Medium/Low based on data quality and sample size]

Step 6: Output Format

Present results in this order:

1. Executive Summary (3-5 bullet points of the most important findings)
2. Dataset Overview (shape, types, quality score)
3. Key Statistics Table (formatted summary stats)
4. Top Insights (ranked by impact, using the insight template above)
5. Correlations and Relationships (heatmap description or table)
6. Anomalies and Outliers (flagged records with explanation)
7. Data Quality Report (issues found, severity, remediation)
8. Recommended Next Steps (further analyses, data collection, actions)

Quality Checklist

Before delivering results, verify:

• All columns have been examined
• Missing data has been quantified and patterns noted
• At least 3 actionable insights are provided
• Statistics are appropriate for the data types (no mean of categorical data)
• Outliers have been identified and contextualized
• Findings distinguish correlation from causation
• Confidence levels are stated for each insight
• Recommendations are specific and actionable

Edge Cases

• Small datasets (< 30 rows): Warn about limited statistical power, avoid complex tests
• High-cardinality categoricals: Group into top-N + "Other"
• Heavily skewed data: Use median over mean, consider log transforms
• Time series with gaps: Note gaps explicitly, do not interpolate without stating assumptions
• Mixed types in columns: Flag and clean before analysis
• All nulls in a column: Flag for removal, do not include in statistics

Tools and Libraries

Prefer these for implementation:

• Python: pandas, numpy, scipy.stats, scikit-learn
• Visualization: matplotlib, seaborn, plotly
• SQL: Use window functions, CTEs, and GROUP BY for aggregation
• R: dplyr, tidyr, ggplot2 (if user prefers R)

Always show your code and explain your reasoning at each step.