data-exploration-key-columns
Sub-skill of data-exploration: Key Columns (+5).
Best use case
data-exploration-key-columns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-exploration: Key Columns (+5).
Teams using data-exploration-key-columns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/key-columns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-exploration-key-columns Compares
| Feature / Agent | data-exploration-key-columns | 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?
Sub-skill of data-exploration: Key Columns (+5).
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
# Key Columns (+5) ## 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] ``` ## Schema Exploration Queries When connected to a data warehouse, use these patterns to discover schema: ```sql -- 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 ``` ## Lineage and Dependencies When exploring an unfamiliar data environment: 1. Start with the "output" tables (what reports or dashboards consume) 2. Trace upstream: What tables feed into them? 3. Identify raw/staging/mart layers 4. Map the transformation chain from raw data to analytical tables 5. Note where data is enriched, filtered, or aggregated
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