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D1 Drizzle Schema

CommunityPopular
jezweb
d1-drizzle-schema

Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.

Overview

Publisherjezweb
Repositoryclaude-skills
Skill named1-drizzle-schema
Stars
1K
Forks
102
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jezweb on GitHub. Read the source before you install it.

Installation

Install the D1 Drizzle Schema AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jezweb/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/plugins/cloudflare/skills/d1-drizzle-schema .claude/skills/d1-drizzle-schema
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable D1 Drizzle Schema in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads D1 Drizzle Schema on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so D1 Drizzle Schema is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

D1 Drizzle Schema

Generate correct Drizzle ORM schemas for Cloudflare D1. D1 is SQLite-based but has important differences that cause subtle bugs if you use standard SQLite patterns. This skill produces schemas that work correctly with D1's constraints.

Critical D1 Differences

FeatureStandard SQLiteD1
Foreign keysOFF by defaultAlways ON (cannot disable)
Boolean typeNoNo — use integer({ mode: 'boolean' })
Datetime typeNoNo — use integer({ mode: 'timestamp' })
Max bound params~999100 (affects bulk inserts)
JSON supportExtensionAlways available (json_extract, ->, ->>)
ConcurrencyMulti-writerSingle-threaded (one query at a time)

Workflow

Step 1: Describe the Data Model

Gather requirements: what tables, what relationships, what needs indexing. If working from an existing description, infer the schema directly.

Step 2: Generate Drizzle Schema

Create schema files using D1-correct column patterns:

typescript
import { sqliteTable, text, integer, real, index, uniqueIndex } from 'drizzle-orm/sqlite-core'

export const users = sqliteTable('users', {
  // UUID primary key (preferred for D1)
  id: text('id').primaryKey().$defaultFn(() => crypto.randomUUID()),

  // Text fields
  name: text('name').notNull(),
  email: text('email').notNull(),

  // Enum (stored as TEXT, validated at schema level)
  role: text('role', { enum: ['admin', 'editor', 'viewer'] }).notNull().default('viewer'),

  // Boolean (D1 has no BOOL — stored as INTEGER 0/1)
  emailVerified: integer('email_verified', { mode: 'boolean' }).notNull().default(false),

  // Timestamp (D1 has no DATETIME — stored as unix seconds)
  createdAt: integer('created_at', { mode: 'timestamp' }).notNull().$defaultFn(() => new Date()),
  updatedAt: integer('updated_at', { mode: 'timestamp' }).notNull().$defaultFn(() => new Date()),

  // Typed JSON (stored as TEXT, Drizzle auto-serialises)
  preferences: text('preferences', { mode: 'json' }).$type<UserPreferences>(),

  // Foreign key (always enforced in D1)
  organisationId: text('organisation_id').references(() => organisations.id, { onDelete: 'cascade' }),
}, (table) => ({
  emailIdx: uniqueIndex('users_email_idx').on(table.email),
  orgIdx: index('users_org_idx').on(table.organisationId),
}))

See references/column-patterns.md for the full type reference.

Step 3: Add Relations

Drizzle relations are query builder helpers (separate from FK constraints):

typescript
import { relations } from 'drizzle-orm'

export const usersRelations = relations(users, ({ one, many }) => ({
  organisation: one(organisations, {
    fields: [users.organisationId],
    references: [organisations.id],
  }),
  posts: many(posts),
}))

Step 4: Export Types

typescript
export type User = typeof users.$inferSelect
export type NewUser = typeof users.$inferInsert

Step 5: Set Up Drizzle Config

Copy assets/drizzle-config-template.ts to drizzle.config.ts and update the schema path.

Step 6: Add Migration Scripts

Add to package.json:

json
{
  "db:generate": "drizzle-kit generate",
  "db:migrate:local": "wrangler d1 migrations apply DB --local",
  "db:migrate:remote": "wrangler d1 migrations apply DB --remote"
}

Always run on BOTH local AND remote before testing.

Step 7: Generate DATABASE_SCHEMA.md

Document the schema for future sessions:

  • Tables with columns, types, and constraints
  • Relationships and foreign keys
  • Indexes and their purpose
  • Migration workflow

Bulk Insert Pattern

D1 limits bound parameters to 100. Calculate batch size:

typescript
const BATCH_SIZE = Math.floor(100 / COLUMNS_PER_ROW)
for (let i = 0; i < rows.length; i += BATCH_SIZE) {
  await db.insert(table).values(rows.slice(i, i + BATCH_SIZE))
}

D1 Runtime Usage

typescript
import { drizzle } from 'drizzle-orm/d1'
import * as schema from './schema'

// In Worker fetch handler:
const db = drizzle(env.DB, { schema })

// Query patterns
const all = await db.select().from(schema.users).all()           // Array<User>
const one = await db.select().from(schema.users).where(eq(schema.users.id, id)).get()  // User | undefined
const count = await db.select({ count: sql`count(*)` }).from(schema.users).get()

Reference Files

WhenRead
D1 vs SQLite, JSON queries, limitsreferences/d1-specifics.md
Column type patterns for Drizzle + D1references/column-patterns.md

Assets

FilePurpose
assets/drizzle-config-template.tsStarter drizzle.config.ts for D1
assets/schema-template.tsExample schema with all common D1 patterns

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the D1 Drizzle Schema AI skill do?

Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.

Why use D1 Drizzle Schema on TypingMind?

Because you install it once and use it with any model. D1 Drizzle Schema is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install D1 Drizzle Schema in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jezweb/claude-skills/tree/main/plugins/cloudflare/skills/d1-drizzle-schema. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use D1 Drizzle Schema?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with D1 Drizzle Schema?

As many as you like. As long as a model supports skills, you can use D1 Drizzle Schema with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the D1 Drizzle Schema AI skill free?

Yes. It is published on GitHub by jezweb under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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