data-exploration

SKILL.md 节选

# Data Exploration Skill

Systematic methodology for profiling datasets, assessing data quality, discovering patterns, and understanding schemas.

## Data Profiling Methodology

### Phase 1: Structural Understanding

Before analyzing any data, understand its structure:

**Table-level questions:**
- How many rows and columns?
- What is the grain (one row per what)?
- What is the primary key? Is it unique?
- When was the data last updated?
- How far back does the data go?

**Column classification:**
Categorize each column as one of:
- **Identifier**: Unique keys, foreign keys, entity IDs
- **Dimension**: Categorical attributes for grouping/filtering (status, type, region, category)
- **Metric**: Quantitative values for measurement (revenue, count, duration, score)
- **Temporal**: Dates and timestamps (created_at, updated_at, event_date)
- **Text**: Free-form text fields (description, notes, name)
- **Boolean**: True/false flags
- **Structural**: JSON, arrays, nested structures

### Phase 2: Column-Level Profiling

For each column, compute:

**All columns:**
- Null count and null rate
- Distinct count and cardinality ratio (distinct / total)
- Most common values (top 5-10 with frequencies)
- Least common values (bottom 5 to spot anomalies)

**Numeric columns (metrics):**
```
min, max, mean, median (p50)
standard deviation
percentiles: p1, p5, p25, p75, p95, p99
zero count
negative count (if unexpected)
```

**String columns (dimensions, text):**
```
min length, max length, avg length
empty string count
pattern analysis (do values follow a format?)
case consistency (all upper, all lower, mixed?)
leading/trailing whitespace count
```

**Date/timestamp columns:**
```
min date, max date
null dates
future dates (if unexpected)
distribution by month/week
gaps in time series
```

**Boolean columns:**
```
true count, false count, null count
true rate
```

### Phase 3: Relationship Discovery

After profiling individual columns:

- **Foreign key candidates**: ID columns that might link to other tables
- **Hierarchies**: Columns that form natural drill-down paths (country > state > city)
- **Correlations**: Numeric columns that move together
- **Derived columns**: Columns that appear to be computed from others
- **Redundant columns**: Columns with identical or near-identical information

## Quality Assessment Framework

### Completeness Score

Rate each column:
- **Complete** (>99% non-null): Green
- **Mostly complete** (95-99%): Yellow -- investigate the nulls
- **Incomplete** (80-95%): Orange -- understand why and whether it matters
- **Sparse** (<80%): Red -- may not be usable without imputation

### Consistency Checks

Look for:
- **Value format inconsistency**: Same concept represented differently ("USA", "US", "United States", "us")
- **Type inconsistency**: Numbers stored as strings, dates in various formats
- **Referential integrity**: Foreign keys that don't match any parent record
- **Business rule violations**: Negative quantities, end dates before start dates, percentages > 100
- **Cross-column consistency**: Status = "completed" but completed_at is null

### Accuracy Indicators

Red flags that suggest accuracy issues:
- **Placeholder values**: 0, -1, 999999, "N/A", "TBD", "test", "xxx"
- **Default values**: Suspiciously high frequency of a single value
- **Stale data**: Updated_at shows no recent changes in an active system
- **Impossible values**: Ages > 150, dates in the far future, negative durations
- **Round number bias**: All values ending in 0 or 5 (suggests estimation, not measurement)

### Timeliness Assessment

- When was the table last updated?
- What is the expected update frequency?
- Is there a lag between event time and load time?
- Are there gaps in the time series?

## Pattern Discovery Techniques

### Distribution Analysis

For numeric columns, characterize the distribution:
- **Normal**: Mean and median are close, bell-shaped
- **Skewed right**: Long tail of high values (common for revenue, session duration)
- **Skewed left**: Long tail of low values (less common)

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