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drizzle-orm skill

/skills/drizzle-orm

This skill delivers concise Drizzle ORM guidance from official docs, clarifying headless ORM concepts, SQL-like versus not, and onboarding strategies.

npx playbooks add skill bankkroll/skills-builder --skill drizzle-orm

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SKILL.md
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---
name: "drizzle-orm"
description: "Scraped from https://orm.drizzle.team/docs/overview/ Source: https://orm.drizzle.team/docs/overview."
---

# Drizzle Orm

> Official documentation: https://orm.drizzle.team/docs/overview

## Overview

This skill provides comprehensive documentation for drizzle orm.

**Total references:** 1 files (~1,124 tokens)

**Topics covered:**
Why SQL-like, Serverless, Why not SQL-like, Welcome on board, Headless ORM

## Reference Files

Load only the reference files relevant to the user's question:

- **[Drizzle ORM](references/general.md)** (~1,124 tokens)
  - Topics: Headless ORM, Why SQL-like, Why not SQL-like

## Usage Guidelines

1. **Identify relevant sections** - Match the user's question to the appropriate reference file(s)
2. **Load minimally** - Only read files directly relevant to the question to conserve context
3. **Cite sources** - Reference specific sections when answering
4. **Combine knowledge** - For complex questions, you may need multiple reference files

### When to use each reference:

- **General**: General documentation, overview, and getting started

Overview

This skill documents Drizzle ORM, a modern, headless ORM designed for SQL-first workflows and serverless-friendly applications. It summarizes core concepts including why a SQL-like approach is favored, trade-offs, and the headless design that decouples query construction from runtime execution. The content helps developers decide when to adopt Drizzle and how it fits into serverless or traditional stacks.

How this skill works

The skill inspects the ORM's philosophy and core features: a SQL-like API for explicit queries, a headless layer that emits SQL rather than hiding it, and design choices optimized for serverless environments. It highlights the rationale for preferring SQL-like patterns, reasons some teams might avoid them, and practical implications for migrations and integrations. The documentation is organized to guide developers from conceptual trade-offs to concrete usage scenarios.

When to use it

  • When you want full control over generated SQL while retaining ergonomics of an ORM
  • Building serverless or edge applications where connection management and cold starts matter
  • When explicit, auditable queries are required for compliance or debugging
  • Migrating from raw SQL or query builders to a typed, structured ORM layer
  • When you prefer a headless design that separates query authoring from execution/runtime

Best practices

  • Treat the ORM as SQL-first: write clear query shapes and inspect emitted SQL during development
  • Use the headless layer to integrate with different execution environments (serverless functions, client adapters)
  • Keep connection and pooling logic outside the ORM where possible in serverless contexts
  • Adopt typed schemas and migrations to reduce runtime surprises and improve refactorability
  • Benchmark critical queries and avoid over-abstracting complex joins or performance-sensitive operations

Example use cases

  • A serverless API that needs predictable SQL and minimal runtime footprint
  • An application requiring strict observability of every generated query for auditing
  • A team migrating legacy SQL to a typed ORM while keeping SQL semantics intact
  • Edge or SSR rendering that benefits from headless query generation and flexible execution hooks
  • A microservice that must integrate with multiple databases or execution layers using the same query definitions

FAQ

Is Drizzle suitable for serverless apps?

Yes. Drizzle's headless design and explicit SQL behavior make it well suited for serverless environments where connection handling and startup performance are important considerations.

Why choose a SQL-like ORM instead of a fully abstracted one?

A SQL-like ORM preserves clarity and control over generated queries, reduces surprising behavior, and simplifies debugging and optimization compared with heavy abstractions that hide SQL.