# Schema Exploration

Explore an existing PostgreSQL database before answering questions about its data or...

- Repository: https://github.com/timescale/pg-aiguide (1877 stars)
- Category: Design
- License: Apache-2.0
- Last updated: 2026-10-28
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add timescale/pg-aiguide --skill schema-exploration -g -a claude-code -y`
Codex: `npx skills add timescale/pg-aiguide --skill schema-exploration -g -a codex -y`
Cursor: `npx skills add timescale/pg-aiguide --skill schema-exploration -g -a cursor -y`
Gemini CLI: `npx skills add timescale/pg-aiguide --skill schema-exploration -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add timescale/pg-aiguide`

## Description

Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Explore a PostgreSQL database

Start with the user's question, not a full-database audit unless that is the explicit ask. Use the database connection already available to you; if none is available, ask for access or work from provided schema files. Stay within the database and schemas the user has authorized. Catalog metadata can reveal sensitive names or source code: do not export it unnecessarily.

## Workflow

1. Establish the current database, server version, role, and scope. Use [overview](references/overview.md) for a small inventory. Choose relevant schemas; do not assume `public` contains the application.
2. Pick the most promising object(s) and read **only** the matching drill-down reference: [tables](references/tables.md), [partitioning/inheritance](references/partitioning-and-inheritance.md), [foreign tables](references/foreign-tables.md), [views](references/views.md), [routines](references/routines.md), [triggers](references/triggers.md), [security/RLS](references/security.md), or [extensions](references/extensions.md). Follow cross-references only when the question requires them.
3. Corroborate meaning with comments, definitions, keys, and known dependencies. Names are clues, not proof of business meaning. If data-derived values would help, use [data-derived values](references/data-values.md) **only after obtaining approval** for the specific columns and access method. Ask the user when semantics remain ambiguous.
4. If the user wants SQL, follow [query authoring](references/query-authoring.md): establish join keys and grain, then validate a vetted read-only query with plain `EXPLAIN` where authorized. Do not mistake a valid plan for proof of business semantics.
5. Stop once you can answer. Report the specific schema-qualified objects and evidence, distinguish observations from inferences, and state limitations (permissions, stale statistics, missing dependencies, unknown application logic).

## Example finding

The rest: https://github.com/timescale/pg-aiguide/blob/HEAD/skills/schema-exploration/SKILL.md