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Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
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基于 SOC 职业分类
| name | data-exploration |
| description | Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze. |
Systematic methodology for profiling datasets, assessing data quality, discovering patterns, and understanding schemas.
Before analyzing any data, understand its structure:
Table-level questions:
Column classification: Categorize each column as one of:
For each column, compute:
All columns:
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
After profiling individual columns:
Rate each column:
Look for:
Red flags that suggest accuracy issues:
For numeric columns, characterize the distribution:
For time series data, look for:
Identify natural segments by:
Between numeric columns:
When documenting a dataset for team use:
## Table: [schema.table_name]
**Description**: [What this table represents]
**Grain**: [One row per...]
**Primary Key**: [column(s)]
**Row Count**: [approximate, with date]
**Update Frequency**: [real-time / hourly / daily / weekly]
**Owner**: [team or person responsible]
### Key Columns
| Column | Type | Description | Example Values | Notes |
|--------|------|-------------|----------------|-------|
| user_id | STRING | Unique user identifier | "usr_abc123" | FK to users.id |
| event_type | STRING | Type of event | "click", "view", "purchase" | 15 distinct values |
| revenue | DECIMAL | Transaction revenue in USD | 29.99, 149.00 | Null for non-purchase events |
| created_at | TIMESTAMP | When the event occurred | 2024-01-15 14:23:01 | Partitioned on this column |
### Relationships
- Joins to `users` on `user_id`
- Joins to `products` on `product_id`
- Parent of `event_details` (1:many on event_id)
### Known Issues
- [List any known data quality issues]
- [Note any gotchas for analysts]
### Common Query Patterns
- [Typical use cases for this table]
When connected to a data warehouse, use these patterns to discover schema:
-- List all tables in a schema (PostgreSQL)
SELECT table_name, table_type
FROM information_schema.tables
WHERE table_schema = 'public'
ORDER BY table_name;
-- Column details (PostgreSQL)
SELECT column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_name = 'my_table'
ORDER BY ordinal_position;
-- Table sizes (PostgreSQL)
SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC;
-- Row counts for all tables (general pattern)
-- Run per-table: SELECT COUNT(*) FROM table_name
When exploring an unfamiliar data environment:
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
Build self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling. Use when creating dashboards, building interactive reports, or generating shareable HTML files with charts and filters that work without a server.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
Apply and enforce brand voice, style guide, and messaging pillars across content. Use when reviewing content for brand consistency, documenting a brand voice, adapting tone for different audiences, or checking terminology and style guide compliance.