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    PostgreSQL Schema Architect

    1

    Schema mistakes are the most expensive kind — code refactors in days, but a wrong table design holds your data hostage for years.

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    PostgreSQL Schema Architect

    PostgreSQL Schema Architect

    Example session with this skill installed

    Design the Postgres schema for a multi-tenant B2B invoicing app: tenants, customers, invoices with line items, and payments. The main queries are listing a tenant's unpaid invoices by due date and monthly revenue reports. We expect a few million invoices per year.

    • Read your context and instructions
    • Compiled the postgresql schema architect

    Schema for multi-tenant invoicing: tenant_id on every table, composite keys for tenant scoping, and monthly range partitioning on invoices.

    CREATE TABLE tenant (
      tenant_id  bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
      name       text NOT NULL
    );
    
    CREATE TABLE customer (
      tenant_id   bigint NOT NULL REFERENCES tenant,
      customer_id bigint GENERATED ALWAYS AS IDENTITY,
      name        text NOT NULL,
      PRIMARY KEY (tenant_id, customer_id)
    );
    
    CREATE TABLE invoice (
      tenant_id    bigint NOT NULL,
      invoice_id   bigint GENERATED ALWAYS AS IDENTITY,
      customer_id  bigint NOT NULL,
      issued_on    date NOT NULL,
      due_on       date NOT NULL CHECK (due_on >= issued_on),
      status       text NOT NULL CHECK (status IN ('open','paid','void')),
      total_cents  bigint NOT NULL CHECK (total_cents >= 0),
      PRIMARY KEY (tenant_id, invoice_id, issued_on),
      FOREIGN KEY (tenant_id, customer_id) REFERENCES customer
    ) PARTITION BY RANGE (issued_on);
    
    CREATE INDEX invoice_unpaid_due_idx
      ON invoice (tenant_id, due_on) WHERE status = 'open';
    

    Design notes

    • invoice_line and payment follow the same pattern, amounts in integer cents.
    • The partial index serves the unpaid list by due date.
    • Monthly partitions on issued_on keep revenue reports to few partitions.
    • Naming: singular snake_case tables, <table>_id keys.

    Next steps

    • Create partitions ahead of time with a scheduled job or pg_partman.
    • Add row-level security on tenant_id for one shared DB role.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Schema mistakes are the most expensive kind — code refactors in days, but a wrong table design holds your data hostage for years. This skill designs your PostgreSQL schema from the ground up: table structure, normalization decisions and where to deliberately denormalize, index strategy for your actual query patterns, constraint design that enforces business rules at the database level, and the migration path if you're evolving an existing schema. It explains every decision in terms of the trade-offs specific to your use case — not generic best practices, but choices calibrated to your data volume, query patterns, and growth trajectory. Use it before you write your first CREATE TABLE, or when you've hit the performance wall that tells you the original design was wrong. Give it your domain model and your most expensive queries; it returns a schema you can defend.

    • Does not perform live database migrations or execution.
    • Limited support for niche extensions outside PostGIS/pg_vector.
    • Cannot predict exact hardware-level IOPS bottlenecks.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

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    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed4 months ago
    Updated4 months ago

    What's inside

    Frequently Asked Questions

    This skill designs complete PostgreSQL schemas, including normalization strategy, indexing for specific query patterns, and constraint design, specifically to prevent long-term technical debt caused by poor data modeling.

    The skill provides a complete schema blueprint (DDL), an indexing strategy, a detailed explanation of trade-offs for your specific use case, and a migration path if you are evolving an existing structure.

    Yes, it can analyze your current schema and most 'expensive' queries to identify bottlenecks, proposing a refactored design that balances performance with data integrity.

    This skill is a standard AI agent configuration compatible with most major agentic frameworks and orchestration platforms that support PostgreSQL-focused tasks.

    It is designed for PostgreSQL 12 and above, taking advantage of modern features like JSONB optimization, declarative partitioning, and advanced indexing types where appropriate.

    No complex setup is required; once purchased, you can immediately input your business requirements or entity-relationship diagrams to begin the design process.