Validate with Database
Validates database schema assumptions and cross-references DDL implementations.
Install
mkdir -p .claude/skills/validate-with-database && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11507" && unzip -o skill.zip -d .claude/skills/validate-with-database && rm skill.zipInstalls to .claude/skills/validate-with-database
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Connect to live PostgreSQL database to validate schema assumptions, compare pg_dump vs pgschema output, and query system catalogs interactivelyKey capabilities
- →Connect to a test PostgreSQL database
- →Validate assumptions about schema behavior
- →Compare `pg_dump` output with `pgschema` output
- →Query system catalogs interactively
- →Verify system catalog query results
- →Understand how PostgreSQL formats specific DDL
How it works
This skill connects to a test PostgreSQL database using `psql`, `pg_dump`, or `pgschema`. It allows interactive queries, schema exports, and comparisons to validate schema assumptions and debug introspection issues.
Inputs & outputs
When to use Validate with Database
- →Schema validation
- →Comparing DDL
- →Database debugging
- →Migration testing
About this skill
Validate with Database
Use this skill to connect to the test PostgreSQL database, validate assumptions about schema behavior, and cross-validate between pg_dump and pgschema implementations.
When to Use This Skill
Invoke this skill when:
- Validating how PostgreSQL actually stores or represents schema objects
- Comparing pg_dump output with pgschema output
- Testing a new feature implementation against real database
- Debugging schema introspection issues
- Verifying system catalog query results
- Understanding how PostgreSQL formats specific DDL
- Checking version-specific behavior (PostgreSQL 14-17)
- Validating migration plans before implementing new features
Database Connection Information
Connection details are stored in .env file at project root:
PGHOST=localhost
PGDATABASE=employee
PGUSER=postgres
PGPASSWORD=testpwd1
Default connection:
- Host:
localhost - Port:
5432(default) - Database:
employee - User:
postgres - Password:
testpwd1
Connection Methods
Method 1: Using psql (Interactive Queries)
Basic connection:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d employee
One-off query:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d employee -c "SELECT version();"
Execute multi-line query:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
SELECT
t.tgname,
CASE
WHEN t.tgqual IS NOT NULL
THEN pg_get_expr(t.tgqual, t.tgrelid, false)
ELSE 'NO WHEN CLAUSE'
END as when_clause
FROM pg_catalog.pg_trigger t
JOIN pg_catalog.pg_class c ON t.tgrelid = c.oid
WHERE c.relname = 'test_table'
ORDER BY t.tgname;
"
Method 2: Using pg_dump (Schema Export)
Dump entire database schema:
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d employee --schema-only --schema=public
Dump specific table:
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d employee --schema-only --table=employees
Dump only specific object types:
# Only triggers
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d employee --schema-only --schema=public | grep -A 20 "CREATE TRIGGER"
# Only indexes
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d employee --schema-only --schema=public | grep -A 10 "CREATE INDEX"
Method 3: Using pgschema (Project Tool)
Dump with pgschema:
./pgschema dump --host localhost --port 5432 --db employee --user postgres --schema public
Or using environment variables (from .env):
# .env is automatically loaded by pgschema
./pgschema dump --schema public
Dump to file:
./pgschema dump --schema public -o /tmp/schema_dump.sql
Method 4: Database Setup for Testing
Create a test database:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -c "DROP DATABASE IF EXISTS test_validation;"
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -c "CREATE DATABASE test_validation;"
Create test schema objects:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d test_validation -c "
CREATE TABLE test_table (
id SERIAL PRIMARY KEY,
name TEXT NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TRIGGER test_trigger
BEFORE INSERT ON test_table
FOR EACH ROW
WHEN (NEW.name IS NOT NULL)
EXECUTE FUNCTION my_trigger_func();
"
Common Validation Workflows
Workflow 1: Compare pg_dump vs pgschema Output
Purpose: Verify pgschema produces comparable output to pg_dump
Steps:
- Dump with pg_dump:
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d employee --schema-only --schema=public > /tmp/pg_dump_output.sql
- Dump with pgschema:
./pgschema dump --schema public -o /tmp/pgschema_output.sql
- Compare outputs:
# Side-by-side comparison
diff -u /tmp/pg_dump_output.sql /tmp/pgschema_output.sql
# Or use a better diff tool
code --diff /tmp/pg_dump_output.sql /tmp/pgschema_output.sql
- Analyze differences:
- Formatting differences (expected)
- Missing objects (bugs to fix)
- Different DDL structure (may need investigation)
- Comments handling
- Ordering differences
Workflow 2: Validate System Catalog Queries
Purpose: Test system catalog queries return expected data
Steps:
- Create test object:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
CREATE TABLE test_triggers (
id INTEGER PRIMARY KEY,
data TEXT
);
CREATE OR REPLACE FUNCTION trigger_func() RETURNS TRIGGER AS \$\$
BEGIN
RETURN NEW;
END;
\$\$ LANGUAGE plpgsql;
CREATE TRIGGER test_when_trigger
BEFORE INSERT ON test_triggers
FOR EACH ROW
WHEN (NEW.data <> '')
EXECUTE FUNCTION trigger_func();
"
- Query system catalogs:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
SELECT
t.tgname,
t.tgtype,
CASE
WHEN t.tgqual IS NOT NULL
THEN pg_get_expr(t.tgqual, t.tgrelid, false)
ELSE NULL
END as when_clause,
pg_get_triggerdef(t.oid) as full_definition
FROM pg_catalog.pg_trigger t
JOIN pg_catalog.pg_class c ON t.tgrelid = c.oid
WHERE c.relname = 'test_triggers'
AND t.tgisinternal = false;
"
- Verify pgschema extracts same data:
./pgschema dump --schema public | grep -A 20 "test_when_trigger"
- Cleanup:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
DROP TRIGGER IF EXISTS test_when_trigger ON test_triggers;
DROP TABLE IF EXISTS test_triggers;
DROP FUNCTION IF EXISTS trigger_func();
"
Workflow 3: Test Plan/Apply Workflow
Purpose: Validate pgschema plan and apply work correctly
Steps:
- Create initial schema:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
DROP SCHEMA IF EXISTS test_workflow CASCADE;
CREATE SCHEMA test_workflow;
SET search_path TO test_workflow;
CREATE TABLE users (
id SERIAL PRIMARY KEY,
email TEXT NOT NULL UNIQUE
);
"
- Dump current state:
./pgschema dump --schema test_workflow -o /tmp/current_schema.sql
- Modify schema file (edit /tmp/current_schema.sql):
-- Add a new column
CREATE TABLE users (
id SERIAL PRIMARY KEY,
email TEXT NOT NULL UNIQUE,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -- NEW
);
- Generate plan:
./pgschema plan --schema test_workflow --file /tmp/current_schema.sql
- Review migration DDL - should show:
ALTER TABLE users ADD COLUMN created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP;
- Apply migration:
./pgschema apply --schema test_workflow --file /tmp/current_schema.sql --auto-approve
- Verify result:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "\d test_workflow.users"
- Cleanup:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "DROP SCHEMA IF EXISTS test_workflow CASCADE;"
Workflow 4: Validate Specific DDL Formatting
Purpose: Understand how PostgreSQL formats specific constructs
Steps:
- Create object with specific feature:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
CREATE TABLE test_pk_order (
b INTEGER,
a INTEGER,
c INTEGER,
PRIMARY KEY (a, b) -- Note: different order than column definition
);
"
- Check how PostgreSQL stores it:
# Use \d+ to see structure
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "\d+ test_pk_order"
- See pg_dump format:
PGPASSWORD='testpwd1' pg_dump -h localhost -p 5432 -U postgres -d postgres --schema-only --table=test_pk_order
- Query system catalogs directly:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
SELECT
c.relname as table_name,
con.conname as constraint_name,
pg_get_constraintdef(con.oid) as constraint_def
FROM pg_constraint con
JOIN pg_class c ON con.conrelid = c.oid
WHERE c.relname = 'test_pk_order';
"
- Compare with pgschema:
./pgschema dump --schema public | grep -A 10 "test_pk_order"
Workflow 5: Cross-Version Testing
Purpose: Validate behavior across PostgreSQL versions 14-17
Steps:
- Check current version:
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "SELECT version();"
- Run version-specific integration tests:
# Test against specific version
PGSCHEMA_POSTGRES_VERSION=14 go test -v ./cmd/dump -run TestDumpCommand_Employee
PGSCHEMA_POSTGRES_VERSION=17 go test -v ./cmd/dump -run TestDumpCommand_Employee
- Check for version-specific features:
# PostgreSQL 15+ feature: UNIQUE NULLS NOT DISTINCT
PGPASSWORD='testpwd1' psql -h localhost -p 5432 -U postgres -d postgres -c "
SELECT version();
CREATE TABLE test_nulls (
id INTEGER,
email TEXT UNIQUE NULLS NOT DISTINCT
);
"
Useful System Catalog Queries
Inspect Tables and Columns
-- All tables in schema
SELECT schemaname, tablename
FROM pg_tables
WHERE schemaname = 'public';
-- Columns with types
SELECT
a.attname as column_name,
pg_catalog.format_type(a.atttypid, a.atttypmod) as data_type,
a.attnotnull as not_null,
pg_get_expr(ad.adbin, ad.adrelid) as default_value,
a.attgenerated as generated
FROM pg_attribute a
LEFT JOIN pg_attrdef ad ON (a.attrelid = ad.adrelid AND a.attnum = ad.adnum)
WHERE a.attrelid = 'public.employees'::regclass
AND a.attnum > 0
AND NOT a.attisdropped
ORDER BY a.attnum;
Inspect Constraints
-- All constraints on a table
SELECT
con.conname as constraint_name,
con.contype as constraint_type,
pg_get_constraintdef(con.oid) as definition
FR
---
*Content truncated.*
When not to use it
- →When the task does not involve PostgreSQL database validation
- →When the user does not need to compare schema outputs or query system catalogs
- →When the user does not need to debug migration or schema mapping issues
Limitations
- →Connection details are stored in a `.env` file at the project root.
- →The skill supports `psql` for interactive queries, `pg_dump` for schema export, and `pgschema` for project-specific dumps.
- →It can be used to compare `pg_dump` vs `pgschema` output.
How it compares
This skill provides direct interactive access and comparison tools for PostgreSQL schema validation, unlike relying solely on application-level schema definitions.
Compared to similar skills
Validate with Database side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| Validate with Database (this skill) | 0 | 5mo | Review | Advanced |
| snowflake-semanticview | 5 | 6mo | Review | Advanced |
| setup-timescaledb-hypertables | 0 | 4mo | No flags | Advanced |
| comparing-database-schemas | 1 | 27d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by diegosouzapw
View all by diegosouzapw →You might also like
snowflake-semanticview
github
Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic-view DDL against Snowflake via CLI, or to guide Snowflake CLI installation and connection setup.
setup-timescaledb-hypertables
timescale
Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.
comparing-database-schemas
jeremylongshore
Process use when you need to work with schema comparison. This skill provides database schema diff and sync with comprehensive guidance and automation. Trigger with phrases like "compare schemas", "diff databases", or "sync database schemas".
database-schema-design
RepairYourTech
Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for any database paradigm.
drizzle-orm
EpicenterHQ
Drizzle ORM patterns for type branding and custom types. Use when working with Drizzle column definitions, branded types, or custom type conversions.
database-design
davila7
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.