{"id":"e3284325-9342-437c-9091-47c8b7026c8f","shortId":"M8TtZy","kind":"skill","title":"exploring-data","tagline":"Explores data connections and schemas. Use when asking about tables, columns, data types, data structure, or available sources before querying.","description":"# Exploring Data\n\n## Quick Start\n\nTo explore available data:\n1. List all connections to see available data sources\n2. Get connection details to see schemas, tables, and columns\n3. List semantic models to discover pre-defined metrics and dimensions\n\n## When to Use This Skill\n\n- User asks \"what data do I have?\"\n- User wants to understand table structure\n- Before writing queries to understand available columns\n- When onboarding a new data source\n- User asks about available connections or databases\n\n## Core Workflow\n\n### Step 1: List Available Connections\n\nList all connections via the Altertable MCP server. Each connection has a name, engine type, and slug.\n\nBuilt-in connections include `altertable` (platform data) and `sample-data`.\n\n### Step 2: Get Connection Schema\n\nRetrieve the full schema for a connection of interest:\n- Catalogs, schemas, and tables\n- Column names, data types, and nullability\n- Note the catalog and schema names for query qualification (e.g., `catalog.schema.table`)\n\n### Step 3: Explore Semantic Models\n\nList semantic models to discover pre-defined business logic:\n- Dimensions (categorical attributes for grouping)\n- Measures (aggregations like count, sum, average)\n- Relations (join paths between sources)\n\n## Connection Types\n\n### Data Warehouses\n\n| Engine | Description |\n|--------|-------------|\n| Snowflake | Cloud data warehouse with catalogs and schemas |\n| BigQuery | Google's serverless data warehouse |\n| Redshift | AWS data warehouse |\n\n### Databases\n\n| Engine | Description |\n|--------|-------------|\n| PostgreSQL | Open-source relational database |\n| MySQL / MariaDB | Popular relational databases |\n| Clickhouse | Column-oriented OLAP database |\n\n### Built-in Connections\n\n| Name | Purpose |\n|------|---------|\n| `altertable` | Platform data (events, identities, pageviews) |\n| `sample-data` | Demo data for testing and learning |\n\n## Understanding Schemas\n\n### Table Qualification\n\nTables are referenced using three-part names:\n```\ncatalog.schema.table\n```\n\nExample:\n```sql\nSELECT * FROM my_warehouse.public.users LIMIT 10\n```\n\n### Column Data Types\n\nCommon types across engines:\n- `VARCHAR`, `TEXT`, `STRING` - Text data\n- `INTEGER`, `BIGINT`, `INT64` - Whole numbers\n- `FLOAT`, `DOUBLE`, `NUMERIC` - Decimal numbers\n- `BOOLEAN` - True/false values\n- `TIMESTAMP`, `DATETIME` - Date and time\n- `DATE` - Date only\n- `JSON`, `VARIANT` - Semi-structured data\n\n## Built-in Semantic Sources\n\nThe `altertable` connection includes pre-defined semantic sources:\n\n| Source | Description |\n|--------|-------------|\n| `events` | Product analytics events with properties |\n| `identities` | User identity information |\n| `pageviews` | Web page view events |\n| `sessions` | Web session aggregations |\n| `identity-overrides` | Identity resolution rules |\n\n## Common Patterns\n\n### Discovering Table Purpose\n\nLook for clues in:\n- Table names (e.g., `users`, `orders`, `events`)\n- Column names (e.g., `created_at`, `user_id`, `amount`)\n- Data types (timestamps indicate time-series data)\n\n### Identifying Primary Keys\n\nLook for columns named:\n- `id`, `uuid`, `pk`\n- `{table_name}_id` (e.g., `user_id` in `users` table)\n\n### Finding Relationships\n\nLook for foreign key patterns:\n- `{other_table}_id` columns\n- Matching column names across tables\n- Semantic model relations\n\n## Common Pitfalls\n\n- Assuming table names without checking the schema first\n- Forgetting to qualify tables with catalog.schema\n- Missing that some tables may be views or materialized views\n- Not checking for semantic models that may already define the metrics needed\n\n## Reference Files\n\n- [Connection types detail](references/connection-types.md)\n- [Schema patterns](references/schema-patterns.md)","tags":["exploring","data","skills","altertable-ai","agent-skills","ai-agents","altertable"],"capabilities":["skill","source-altertable-ai","skill-exploring-data","topic-agent-skills","topic-ai-agents","topic-altertable"],"categories":["skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/altertable-ai/skills/exploring-data","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add altertable-ai/skills","source_repo":"https://github.com/altertable-ai/skills","install_from":"skills.sh"}},"qualityScore":"0.453","qualityRationale":"deterministic score 0.45 from registry signals: · indexed on github topic:agent-skills · 7 github stars · SKILL.md body (3,702 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