Schema Exploration — Agent Skill by Timescale (TigerData)

From the author

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.

— the description by tigerdata

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a systematic approach for AI agents to investigate unfamiliar PostgreSQL databases. It emphasizes read-only catalog exploration to understand schema structure, business concepts, and object relationships. By prioritizing metadata analysis over direct data inspection, it ensures safe, authorized, and context-aware interactions with database environments, requiring explicit user approval before accessing data-derived statistics or row samples.

What it does

Inventory database schemas, tables, views, and routines using read-only catalog queries.
Corroborate business meaning through comments, keys, and dependency analysis.
Request and manage user approval for accessing data-derived statistics or row samples.
Validate query plans using EXPLAIN without executing data-modifying operations.
Identify objects owned by installed PostgreSQL extensions.
Report findings while clearly distinguishing between observed facts and inferences.

When to use it

When you need to write SQL for an unfamiliar database schema.
When a user asks for data-backed answers like counts or recent changes.
When you need to locate where specific business concepts reside in the database.
When you need to understand how specific tables, views, triggers, or RLS policies function.

Advantages

Minimizes risk by defaulting to read-only metadata exploration.
Prevents unauthorized data exposure by requiring explicit approval for statistics and row samples.
Provides a clear, repeatable process for mapping database structure to business logic.
Reduces errors in query generation by validating join keys and grains first.
Ensures transparency by reporting limitations and distinguishing observations from assumptions.

Usage examples

“How many active users are in the database?”: The agent will first identify the relevant user table, check its schema and constraints, and then request approval to query statistics or sample rows to provide an accurate count.
“Where is the order history stored?”: The agent will search the catalog for tables or views related to 'orders' or 'history', inspect their columns and foreign keys, and report the likely location with supporting evidence.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/timescale/schema-exploration/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip schema-exploration.zip -d .agents/skills

Documentation by tigerdata

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

Establish the current database, server version, role, and scope. Use overview for a small inventory. Choose relevant schemas; do not assume public contains the application.
Pick the most promising object(s) and read only the matching drill-down reference: tables, partitioning/inheritance, foreign tables, views, routines, triggers, security/RLS, or extensions. Follow cross-references only when the question requires them.
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 only after obtaining approval for the specific columns and access method. Ask the user when semantics remain ambiguous.
If the user wants SQL, follow query authoring: 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.
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

Illustrative only; report facts verified in the target database:

Observed: sales.orders has a primary key on order_id and a foreign key from account_id to sales.accounts.account_id.
Inferred: sales.orders likely records one row per order; the keys support this, but do not establish what the business calls an “order.”
Unresolved: The catalog does not show whether canceled orders remain in this table. Confirm with the application owner before assuming they do.

Safety and execution

Prefer structural catalog queries; pg_stats is data-derived and requires approval too. Do not change schema, data, roles, or session-wide settings without authorization. Never call discovered functions or procedures, refresh materialized views, or run EXPLAIN ANALYZE on unknown queries. A function marked STABLE or IMMUTABLE is not a safety guarantee.
If your client supports transactions, use a read-only transaction and a reasonable statement timeout for exploration. Metadata queries are not a license to run full-table counts or unrestricted scans. Ask before sampling data; sample only when necessary, with explicit limits and a clear understanding of table size and access controls.
All reference queries are plain PostgreSQL SQL. Bind $1, $2, etc. as values using your client's API. psql users can follow the optional psql adapter. Never interpolate an untrusted object name as raw SQL; placeholders cannot replace SQL identifiers in data queries.
Queries target PostgreSQL 18. Each version-sensitive section notes alternatives for older majors where applicable. Check server_version_num first. If a query fails due to permissions or version differences, report the limitation instead of guessing.

Files

ATTRIBUTION.mdAdded by AutoGineer969 B
LICENSELicence file added by AutoGineer9.4 KiB
NOTICELicence file added by AutoGineer573 B
SKILL.md4.3 KiB
references/data-values.md5.7 KiB
references/extensions.md1.8 KiB
references/foreign-tables.md3.9 KiB
references/overview.md5.9 KiB
references/partitioning-and-inheritance.md6.7 KiB
references/psql.md2.1 KiB
references/query-authoring.md2.1 KiB
references/routines.md2.9 KiB
references/security.md2.5 KiB
references/tables.md7.7 KiB
references/triggers.md1.6 KiB
references/views.md2.4 KiB

NOTICE

Copyright 2025 Timescale, Inc., d/b/a Tiger Data

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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