data-exploration
Systematic database and table profiling for DBX Studio. Use when a user wants to understand their data, explore schema structure, or profile a dataset.
Best use case
data-exploration is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Systematic database and table profiling for DBX Studio. Use when a user wants to understand their data, explore schema structure, or profile a dataset.
Systematic database and table profiling for DBX Studio. Use when a user wants to understand their data, explore schema structure, or profile a dataset.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "data-exploration" skill to help with this workflow task. Context: Systematic database and table profiling for DBX Studio. Use when a user wants to understand their data, explore schema structure, or profile a dataset.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-exploration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-exploration Compares
| Feature / Agent | data-exploration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Systematic database and table profiling for DBX Studio. Use when a user wants to understand their data, explore schema structure, or profile a dataset.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Exploration — DBX Studio ## Exploration Workflow ### Phase 1: Schema Discovery Start with `read_schema` to list all tables, then `describe_table` for each table of interest. ``` 1. read_schema(schema_name: "public") 2. describe_table(table_name: "<each table>") 3. get_table_stats(table_name: "<table>") ``` ### Phase 2: Table Profiling For each table, gather: - Row count - Column names and types - Sample data via `get_table_data` - Null counts and distributions ### Phase 3: Relationship Discovery Look for foreign key patterns: - Columns named `*_id` linking to other tables - Common join patterns: `users.id → orders.user_id` ## Quality Scoring | Score | Completeness | |-------|-------------| | Green | > 95% populated | | Yellow | 80–95% populated | | Orange | 50–80% populated | | Red | < 50% populated | ## Common Exploration Queries ### Row count ```sql SELECT COUNT(*) AS row_count FROM "public"."table_name"; ``` ### Column null rates ```sql SELECT COUNT(*) AS total, COUNT(column_name) AS non_null, ROUND(100.0 * COUNT(column_name) / COUNT(*), 2) AS pct_filled FROM "public"."table_name"; ``` ### Distinct values ```sql SELECT column_name, COUNT(*) AS frequency FROM "public"."table_name" GROUP BY 1 ORDER BY 2 DESC LIMIT 20; ``` ### Date range ```sql SELECT MIN(created_at), MAX(created_at) FROM "public"."table_name"; ``` ## Output Format After exploration, present a structured summary: - **Tables**: list with row counts - **Key relationships**: how tables connect - **Data quality flags**: any columns with high null rates - **Suggested next queries**: what the user might want to know next
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