# Exploring Data

> Explores Altertable catalogs, schemas, semantic models, tables, and columns. Use when asking about available data, data structure, connections, or sources before querying.

- **Type:** Skill
- **Install:** `agentstack add skill-altertable-ai-skills-exploring-data`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [altertable-ai](https://agentstack.voostack.com/s/altertable-ai)
- **Installs:** 0
- **Category:** [Search](https://agentstack.voostack.com/c/search)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [altertable-ai](https://github.com/altertable-ai)
- **Source:** https://github.com/altertable-ai/skills/tree/main/skills/exploring-data
- **Website:**  https://altertable.ai

## Install

```sh
agentstack add skill-altertable-ai-skills-exploring-data
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Exploring Data

## Quick Start

To explore available data:
1. Call `initialize` before any other Altertable MCP tool
2. Use `list_catalogs` to see available Altertable databases and external catalogs
3. Use `get_catalog` for schemas, tables, columns, semantic measures, and dimensions
4. Narrow `get_catalog` with `schemas` or `tables` when a catalog is large

## When to Use This Skill

- User asks "what data do I have?"
- User wants to understand table structure
- Before writing queries to understand available columns
- When onboarding a new data source
- User asks about available catalogs, connections, or databases
- User needs to find semantic models, measures, dimensions, or table descriptions

## Core Workflow

### Step 1: Initialize Context

Call `initialize` first. It returns the current organization, environment, and relevant knowledge-entry context. Do not inspect or query data before initialization.

### Step 2: List Available Catalogs

Call `list_catalogs`. Each entry includes:

- `catalog_name` to pass into `get_catalog`
- display name and engine
- optional description

Catalogs can be Altertable-managed databases or external data sources such as Snowflake, BigQuery, Redshift, Postgres, MySQL, MariaDB, object-storage tables, and product analytics.

### Step 3: Get Catalog Schema

Call `get_catalog` for the catalog of interest:

- Schemas and tables
- Column names, data types, and nullability
- Semantic endorsement labels (`draft`, `verified`, `excluded`)
- Semantic dimensions, measures, and relations when available
- Note the catalog and schema names for query qualification (`catalog.schema.table`)

Use `level: overview` for broad discovery, `level: columns` for table shape, and `level: full` for semantic details. For wide catalogs, pass specific `schemas` or `tables`.

### Step 4: Explore Semantic Models

Semantic model details are included in `get_catalog`. Use them to discover pre-defined business logic:

- Dimensions (categorical attributes for grouping)
- Measures (aggregations like count, sum, average)
- Relations (join paths between sources)

## Connection Types

### Data Warehouses

| Engine | Description |
|--------|-------------|
| Snowflake | Cloud data warehouse with catalogs and schemas |
| BigQuery | Google's serverless data warehouse |
| Redshift | AWS data warehouse |

### Databases

| Engine | Description |
|--------|-------------|
| PostgreSQL | Open-source relational database |
| MySQL / MariaDB | Popular relational databases |
| Clickhouse | Column-oriented OLAP database |

### Built-in Catalogs

| Name | Purpose |
|------|---------|
| `product_analytics` | Product events, identities, web sessions, and pageviews when Product Analytics is enabled |
| `opentelemetry` | Logs and traces when OpenTelemetry is enabled |
| User-created catalogs | Managed lakehouse tables and connected external sources |

## Understanding Schemas

### Table Qualification

Tables are referenced using three-part names:
```
catalog.schema.table
```

Example:
```sql
SELECT * FROM my_warehouse.public.users LIMIT 10
```

### Column Data Types

Common types across engines:
- `VARCHAR`, `TEXT`, `STRING` - Text data
- `INTEGER`, `BIGINT`, `INT64` - Whole numbers
- `FLOAT`, `DOUBLE`, `NUMERIC` - Decimal numbers
- `BOOLEAN` - True/false values
- `TIMESTAMP`, `DATETIME` - Date and time
- `DATE` - Date only
- `JSON`, `VARIANT` - Semi-structured data

## Product Analytics Semantic Sources

The `product_analytics` catalog can include pre-defined semantic sources:

| Source | Description |
|--------|-------------|
| `events` | Product analytics events with properties |
| `identities` | User identity information |
| `pageviews` | Web page view events |
| `sessions` | Web session aggregations |
| `identity-overrides` | Identity resolution rules |

## Common Patterns

### Discovering Table Purpose

Look for clues in:
- Table names (e.g., `users`, `orders`, `events`)
- Column names (e.g., `created_at`, `user_id`, `amount`)
- Data types (timestamps indicate time-series data)

### Identifying Primary Keys

Look for columns named:
- `id`, `uuid`, `pk`
- `{table_name}_id` (e.g., `user_id` in `users` table)

### Finding Relationships

Look for foreign key patterns:
- `{other_table}_id` columns
- Matching column names across tables
- Semantic model relations

## Common Pitfalls

- Assuming table names without checking the schema first
- Forgetting to qualify tables with catalog.schema
- Missing that some tables may be views or materialized views
- Querying tables marked `excluded` from the semantic model
- Not checking semantic measures and dimensions that may already define the metrics needed

## Reference Files

- [Connection types detail](references/connection-types.md)
- [Schema patterns](references/schema-patterns.md)

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [altertable-ai](https://github.com/altertable-ai)
- **Source:** [altertable-ai/skills](https://github.com/altertable-ai/skills)
- **License:** MIT
- **Homepage:**  https://altertable.ai

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-altertable-ai-skills-exploring-data
- Seller: https://agentstack.voostack.com/s/altertable-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

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