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Db Seed

CommunityPopular
jezweb
db-seed

Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

Overview

Publisherjezweb
Repositoryclaude-skills
Skill namedb-seed
Stars
1K
Forks
102
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Db Seed 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/db-seed .claude/skills/db-seed
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Db Seed 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 Db Seed 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 Db Seed 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.

Database Seed Generator

Generate seed scripts that populate databases with realistic, domain-appropriate sample data. Reads your schema and produces ready-to-run seed files.

Workflow

1. Find the Schema

Scan the project for schema definitions:

SourceLocation pattern
Drizzle schemasrc/db/schema.ts, src/schema/*.ts, db/schema.ts
D1 migrationsdrizzle/*.sql, migrations/*.sql
Raw SQLschema.sql, db/*.sql
Prismaprisma/schema.prisma

Read all schema files. Build a mental model of:

  • Tables and their columns
  • Data types and constraints (NOT NULL, UNIQUE, DEFAULT)
  • Foreign key relationships (which tables reference which)
  • JSON fields stored as TEXT (common in D1/SQLite)

2. Determine Seed Parameters

Ask the user:

ParameterOptionsDefault
Purposedev, demo, testingdev
Volumesmall (5-10 rows/table), medium (20-50), large (100+)small
Domain context"e-commerce store", "SaaS app", "blog", etc.Infer from schema
Output formatTypeScript (Drizzle), raw SQL, or bothMatch project's ORM

Purpose affects data quality:

  • dev: Varied data, some edge cases (empty fields, long strings, unicode)
  • demo: Polished data that looks good in screenshots and presentations
  • testing: Systematic data covering boundary conditions, duplicates, special characters

3. Plan Insert Order

Build a dependency graph from foreign keys. Insert parent tables before children.

Example order for a blog schema:

1. users        (no dependencies)
2. categories   (no dependencies)
3. posts        (depends on users, categories)
4. comments     (depends on users, posts)
5. tags         (no dependencies)
6. post_tags    (depends on posts, tags)

Circular dependencies: If table A references B and B references A, use nullable foreign keys and insert in two passes (insert with NULL, then UPDATE).

4. Generate Realistic Data

Do NOT use generic placeholders like "test123", "foo@bar.com", or "Lorem ipsum". Generate data that matches the domain.

Data Generation Patterns (no external libraries needed)

Names: Use a hardcoded list of common names. Mix genders and cultural backgrounds.

typescript
const firstNames = ['Sarah', 'James', 'Priya', 'Mohammed', 'Emma', 'Wei', 'Carlos', 'Aisha'];
const lastNames = ['Chen', 'Smith', 'Patel', 'Garcia', 'Kim', 'O\'Brien', 'Nguyen', 'Wilson'];

Emails: Derive from names — sarah.chen@example.com. Use example.com domain (RFC 2606 reserved).

Dates: Generate within a realistic range. Use ISO 8601 format for D1/SQLite.

typescript
const randomDate = (daysBack: number) => {
  const d = new Date();
  d.setDate(d.getDate() - Math.floor(Math.random() * daysBack));
  return d.toISOString();
};

IDs: Use crypto.randomUUID() for UUIDs, or sequential integers if the schema uses auto-increment.

Deterministic seeding: For reproducible data, use a seeded PRNG:

typescript
function seededRandom(seed: number) {
  return () => {
    seed = (seed * 16807) % 2147483647;
    return (seed - 1) / 2147483646;
  };
}
const rand = seededRandom(42); // Same seed = same data every time

Prices/amounts: Use realistic ranges. (rand() * 900 + 100).toFixed(2) for $1-$10 range.

Descriptions/content: Write 3-5 realistic variations per content type and cycle through them. Don't generate AI-sounding prose — write like real user data.

5. Output Format

TypeScript (Drizzle ORM)
typescript
// scripts/seed.ts
import { drizzle } from 'drizzle-orm/d1';
import * as schema from '../src/db/schema';

export async function seed(db: ReturnType<typeof drizzle>) {
  console.log('Seeding database...');

  // Clear existing data (reverse dependency order)
  await db.delete(schema.comments);
  await db.delete(schema.posts);
  await db.delete(schema.users);

  // Insert users
  const users = [
    { id: crypto.randomUUID(), name: 'Sarah Chen', email: 'sarah@example.com', ... },
    // ...
  ];

  // D1 batch limit: 10 rows per INSERT
  for (let i = 0; i < users.length; i += 10) {
    await db.insert(schema.users).values(users.slice(i, i + 10));
  }

  // Insert posts (references users)
  const posts = [
    { id: crypto.randomUUID(), userId: users[0].id, title: '...', ... },
    // ...
  ];

  for (let i = 0; i < posts.length; i += 10) {
    await db.insert(schema.posts).values(posts.slice(i, i + 10));
  }

  console.log(`Seeded: ${users.length} users, ${posts.length} posts`);
}

Run with: npx tsx scripts/seed.ts

For Cloudflare Workers, add a seed endpoint (remove before production):

typescript
app.post('/api/seed', async (c) => {
  const db = drizzle(c.env.DB);
  await seed(db);
  return c.json({ ok: true });
});
Raw SQL (D1)
sql
-- seed.sql
-- Run: npx wrangler d1 execute DB_NAME --local --file=./scripts/seed.sql

-- Clear existing (reverse order)
DELETE FROM comments;
DELETE FROM posts;
DELETE FROM users;

-- Users
INSERT INTO users (id, name, email, created_at) VALUES
  ('uuid-1', 'Sarah Chen', 'sarah@example.com', '2025-01-15T10:30:00Z'),
  ('uuid-2', 'James Wilson', 'james@example.com', '2025-02-01T14:22:00Z');

-- Posts (max 10 rows per INSERT for D1)
INSERT INTO posts (id, user_id, title, body, created_at) VALUES
  ('post-1', 'uuid-1', 'Getting Started', 'Welcome to...', '2025-03-01T09:00:00Z');

6. Idempotency

Seed scripts must be safe to re-run:

typescript
// Option A: Delete-then-insert (simple, loses data)
await db.delete(schema.users);
await db.insert(schema.users).values(seedUsers);

// Option B: Upsert (preserves non-seed data)
for (const user of seedUsers) {
  await db.insert(schema.users)
    .values(user)
    .onConflictDoUpdate({ target: schema.users.id, set: user });
}

Default to Option A for dev/testing, Option B for demo (where users may have added their own data).

D1-Specific Gotchas

GotchaSolution
Max ~10 rows per INSERTBatch inserts in chunks of 10
No native BOOLEANUse INTEGER (0/1)
No native DATETIMEUse TEXT with ISO 8601 strings
JSON stored as TEXTJSON.stringify() before insert
Foreign keys always enforcedInsert parent tables first
100 bound parameter limitKeep batch size × columns < 100

Quality Rules

  1. Match the domain — an e-commerce seed has products with real-sounding names and prices, not "Product 1"
  2. Vary the data — don't make every user "John Smith" or every price "$9.99"
  3. Include edge cases (for testing seeds) — empty strings, very long text, special characters, maximum values
  4. Reference real IDs — foreign keys must point to actually-inserted parent rows
  5. Print what was seeded — always log counts so the user knows it worked
  6. Document the run command — put it in a comment at the top of the file

Frequently asked questions

What does the Db Seed AI skill do?

Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

Why use Db Seed on TypingMind?

Because you install it once and use it with any model. Db Seed 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 Db Seed in TypingMind?

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

Which AI models can use Db Seed?

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 Db Seed?

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

Is the Db Seed 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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