data-exploration-tool
Explore Database Schemas and Profile Data
This skill helps users systematically explore database structures, understand table relationships, and assess data quality through a structured profiling workflow.
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Verwendung von "data-exploration-tool". Explore the public schema
Erwartetes Ergebnis:
- Found 5 tables: users, orders, products, order_items, categories
- Users table: 1,250 rows, 8 columns (id, email, name, created_at, etc.)
- Orders table: 5,432 rows, references users.id via user_id
- Data quality: users table is 98% complete, orders table is 95% complete
Verwendung von "data-exploration-tool". Profile the orders table
Erwartetes Ergebnis:
- Table: orders | Rows: 5,432
- Columns: id (int), user_id (int FK→users), total (decimal), status (varchar), created_at (timestamp)
- Null rates: status=0%, total=2%, user_id=0%
- Date range: 2024-01-01 to 2025-03-10
Sicherheitsaudit
SicherSecurity review complete. Static analyzer flagged 19 potential issues (backticks, crypto keywords, reconnaissance patterns), but manual review confirms all are false positives. The skill contains only markdown documentation with SQL query examples - no actual shell commands, cryptographic code, or reconnaissance tools. This is a legitimate database exploration skill with no security concerns.
Erkannte Muster
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Was du bauen kannst
New Database Onboarding
Quickly understand the structure of a new database by exploring schemas, tables, and their relationships before writing queries.
Data Quality Audit
Assess data completeness by checking null rates, identifying empty columns, and reviewing data distributions across tables.
Query Writing Preparation
Map out table relationships and understand column types before writing complex joins or aggregations.
Probiere diese Prompts
Use the data-exploration skill to explore the schema and tell me what tables are available in the public schema.
Use the data-exploration skill to profile the users table. Include column names, types, and null rates.
Use the data-exploration skill to discover how the orders table relates to other tables. Look for foreign key patterns.
Use the data-exploration skill to run a quality check on all tables in the public schema. Identify tables with high null rates or poor completeness.
Bewährte Verfahren
- Start with read_schema to get a complete table inventory before diving into details
- Use the three-phase workflow (Discovery → Profiling → Relationships) for consistent results
- Check null rates early to identify incomplete data before building queries on those columns
Vermeiden
- Jumping straight to writing queries without first exploring the schema structure
- Assuming column names are intuitive without verifying with describe_table
- Ignoring null rate information when building aggregations or joins