D1 Migration Skill for Claude

Cloudflare D1 migration workflow: generate with Drizzle, inspect SQL for gotchas, apply to local and remote, fix stuck migrations, handle partial failures. Use when running migrations, fixing migration errors, or setting up D1 schemas.

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

953

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/jezweb/claude-skills --skill d1-migration

The instruction itself

15 sections, as written by the author

D1 Migration Workflow

Guided workflow for Cloudflare D1 database migrations using Drizzle ORM.

Standard Migration Flow

1. Generate Migration

pnpm db:generate

This creates a new .sql file in drizzle/ (or your configured migrations directory).

2. Inspect the SQL (CRITICAL)

Always read the generated SQL before applying. Drizzle sometimes generates destructive migrations for simple schema changes.

Red Flag: Table Recreation

If you see this pattern, the migration will likely fail:

CREATE TABLE `my_table_new` (...);
INSERT INTO `my_table_new` SELECT ..., `new_column`, ... FROM `my_table`;
--                                      ^^^ This column doesn't exist in old table!
DROP TABLE `my_table`;
ALTER TABLE `my_table_new` RENAME TO `my_table`;

Cause: Changing a column's default value in Drizzle schema triggers full table recreation. The INSERT SELECT references the new column from the old table.

Fix: If you're only adding new columns (no type/constraint changes on existing columns), simplify to:

ALTER TABLE `my_table` ADD COLUMN `new_column` TEXT DEFAULT 'value';

Edit the .sql file directly before applying.

3. Apply to Local

pnpm db:migrate:local
# or: npx wrangler d1 migrations apply DB_NAME --local

4. Apply to Remote

pnpm db:migrate:remote
# or: npx wrangler d1 migrations apply DB_NAME --remote

Always apply to BOTH local and remote before testing. Local-only migrations cause confusing "works locally, breaks in production" issues.

5. Verify

# Check local
npx wrangler d1 execute DB_NAME --local --command "PRAGMA table_info(my_table)"

# Check remote
npx wrangler d1 execute DB_NAME --remote --command "PRAGMA table_info(my_table)"

Fixing Stuck Migrations

When a migration partially applied (e.g. column was added but migration wasn't recorded), wrangler retries it and fails on the duplicate column.

Symptoms: pnpm db:migrate errors on a migration that looks like it should be done. PRAGMA table_info shows the column exists.

Diagnosis

# 1. Verify the column/table exists
npx wrangler d1 execute DB_NAME --remote \
  --command "PRAGMA table_info(my_table)"

# 2. Check what migrations are recorded
npx wrangler d1 execute DB_NAME --remote \
  --command "SELECT * FROM d1_migrations ORDER BY id"

Fix

# 3. Manually record the stuck migration
npx wrangler d1 execute DB_NAME --remote \
  --command "INSERT INTO d1_migrations (name, applied_at) VALUES ('0013_my_migration.sql', datetime('now'))"

# 4. Run remaining migrations normally
pnpm db:migrate

Prevention

CREATE TABLE IF NOT EXISTS — safe to re-run
ALTER TABLE ADD COLUMN — SQLite has no IF NOT EXISTS variant; check column existence first or use try/catch in application code
Always inspect generated SQL before applying (Step 2 above)

Bulk Insert Batching

D1's parameter limit causes silent failures with large multi-row INSERTs. Batch into chunks:

const BATCH_SIZE = 10;
for (let i = 0; i < allRows.length; i += BATCH_SIZE) {
  const batch = allRows.slice(i, i + BATCH_SIZE);
  await db.insert(myTable).values(batch);
}

Why: D1 fails when rows x columns exceeds ~100-150 parameters.

Column Naming

| Context | Convention | Example |

|---------|-----------|---------|

| Drizzle schema | camelCase | caseNumber: text('case_number') |

| Raw SQL queries | snake_case | UPDATE cases SET case_number = ? |

| API responses | Match SQL aliases | SELECT case_number FROM cases |

New Project Setup

When creating a D1 database for a new project, follow this order:

Deploy Worker first — npm run build && npx wrangler deploy
Create D1 database — npx wrangler d1 create project-name-db
Copy database_id to wrangler.jsonc d1_databases binding
Redeploy — npx wrangler deploy
Run migrations — apply to both local and remote

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take jezweb/d1-migration from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npx. Without those the skill loads but fails at the first command.