| name | add-migration |
| description | Generate and review a Drizzle (or Prisma / Knex / raw SQL) migration safely — classifies the change as additive, mutating, or destructive; for mutating/destructive changes, splits into a multi-phase plan (deploy code that tolerates both shapes → migrate → deploy code that requires the new shape). Catches the landmines: NOT NULL on a populated column without backfill, dropping a column still referenced by old code, adding a unique constraint with existing duplicates, large-table index without `CONCURRENTLY`, type changes that rewrite the table, default values evaluated at migration time vs. row time. Trigger phrases — "add a migration", "schema change", "add column to X", "drop column X", "rename column", "add index", "/add-migration", "drizzle migration", "alter table". Skip for — pure code changes with no schema impact, seed-data-only changes, and infra-level changes (DB version upgrades, replication setup). |
User-question protocol: Whenever this skill needs the user to pick between options, confirm an action, or answer a multiple-choice prompt, you MUST call the AskUserQuestion tool to render a proper interactive picker. Do NOT print numbered options as plain text and wait for the user to type a number — that produces a degraded UX. Free-form questions (open-ended typing) may be asked in prose, but any time you would write "1) … 2) … 3) …", use AskUserQuestion instead.
Mode-less. This skill takes no mode= — callers gate whether to invoke it, not how deep it runs. If you are tempted to add a mode table here, that depth decision belongs to the calling skill.
Code Add Migration
A migration that breaks production usually broke at design time, not at run time. Classify the change first; the plan follows from the class.
Phase 1 — Detect Migration Tooling
In order:
drizzle.config.* → Drizzle (drizzle-kit generate).prisma/schema.prisma → Prisma (prisma migrate dev --create-only).knexfile.* → Knex (knex migrate:make).migrations/ with bare .sql files → raw SQL (project's own runner).
If none detected and the user wants a migration, ask which tool they use rather than picking one.
Exit: the migration tool, command, and output directory are known.
Phase 2 — Classify the Change
MANDATORY — READ references/change-classes.md before classifying. Every change falls into one of three classes; the deployment plan depends on the class.
ADDITIVE — strictly adds. Old code keeps working unchanged.
MUTATING — changes shape of existing rows or constraints. Old code may break.
DESTRUCTIVE — removes data or columns. Cannot be reverted from data alone.
State the class and the one-line reason. If the user's request mixes classes (e.g., "rename column" = drop + add + backfill), split it into separate migrations.
Exit: every requested change has exactly one class.
Phase 3 — Plan by Class
ADDITIVE
Single migration. Examples: new nullable column, new table, new index, new enum value (if the DB supports concurrent enum extension).
Generate the migration. Add the index CONCURRENTLY if the table has any non-trivial size and the DB engine is Postgres. For Drizzle, this requires raw SQL (drizzle-kit does not emit CONCURRENTLY from schema diffs alone).
MUTATING
Multi-step plan. Each step is its own deploy + migration:
- Code tolerates both shapes. Update the application to read/write either old or new shape.
- Backfill / migrate data. Run the migration. For NOT NULL adds: column is added nullable, backfilled, then constrained to NOT NULL in a third step. For type changes: add new column, dual-write, backfill, swap reads, drop old.
- Code requires new shape only. Remove the dual-shape tolerance.
State each step explicitly. Do not collapse them into a single migration even if the table is small today — the pattern is what makes it safe at scale.
DESTRUCTIVE
Multi-step plan with a wait period:
- Stop writing to the column/table. Update code to neither read nor write it. Deploy.
- Verify in production logs/metrics for at least one full deployment cycle (not enforced by this skill — but call it out).
- Drop the column/table. Migration.
For dropping rows (data deletion), get an explicit user y and require a backup pointer (snapshot SHA, dump filename) be recorded in the migration's accompanying notes file.
Exit: the plan is written down with one migration per step.
Phase 4 — Generate
Run the project's generate command. For Drizzle:
pnpm drizzle-kit generate
Open the generated SQL. Read it. drizzle-kit can produce surprising diffs (renames inferred as drop+add, default-value reordering). If the SQL doesn't match the plan, edit it directly — or adjust the schema and regenerate.
For each migration file, verify:
- Index creation on tables with > a few thousand rows uses
CREATE INDEX CONCURRENTLY (Postgres). If the tool can't emit this, hand-edit the SQL and add a note in the migration that it must be run outside a transaction.
- Adding a NOT NULL column has a default OR is followed in a later step by the backfill + constraint.
- Default values:
DEFAULT now() is evaluated per row; DEFAULT '2026-01-01' is a literal. Make sure the right one was generated.
- Foreign-key drops/adds: confirm cascade behavior matches the schema's
references(...).onDelete(...).
Exit: the migration SQL has been read and matches the plan.
Phase 5 — Backfill (if MUTATING)
For backfills that touch many rows:
UPDATE big_table SET new_col = derive(old_col);
UPDATE big_table SET new_col = derive(old_col)
WHERE id BETWEEN <lo> AND <hi>;
For Postgres on tables expected to grow large, use LIMIT + loop or a chunked migration script. The migration runner alone does not chunk.
If the project has no convention for chunked backfills, write the backfill as a separate *.sql file and a one-line note in the migration: -- Run scripts/backfill-<n>.sql before applying the NOT NULL constraint in <next>.sql.
Exit: a backfill plan exists for any column being made NOT NULL on populated data.
Phase 6 — Test the Migration
Run against a local/test DB:
- Restore the DB to a state representative of production (seeded fixtures, ideally a dump).
- Apply the migration.
- Apply the next migration in line, if any.
- Run the app's test suite — at minimum the data-access tests.
If the migration fails to apply on representative data, fix it. If the tests fail, the dual-shape tolerance from Phase 3 step 1 is missing.
Exit: migration applies cleanly on representative data; tests pass.
Phase 7 — Report
Migration added: <file path>
Class: <ADDITIVE / MUTATING / DESTRUCTIVE>
Plan: <step 1>: <this migration>
<step 2>: <next migration's purpose, if multi-step>
<step 3>: <next>
Backfill: <none / inline / scripts/backfill-N.sql>
Concurrent: <yes/no — and why>
Reversible: <yes / no, requires restoring from <backup pointer>>
Apply locally: <command>
Apply in prod: <command> (run during low-traffic window if step requires lock)
If the change was MUTATING or DESTRUCTIVE, remind the user that step 1 must deploy before step 2 runs in production.
Post-step: data-integrity audit
After the migration file and any backfill script are in place, dispatch the reviewer-data-integrity subagent (Agent(subagent_type=reviewer-data-integrity)) against the migration + related code diff. It should check defaults/backfills, destructive rollout order, uniqueness assumptions, delete cascades, seed/test fixture drift, and data-access invariants before the migration is declared shippable.
NEVER
-
NEVER add a NOT NULL column to a populated table in a single step.
Instead: add nullable, backfill, then add the NOT NULL constraint in a follow-up migration.
Why: a single-step NOT NULL on a populated table requires a default. The default applies at migration time and locks the table while every row is rewritten — minutes to hours of downtime on large tables. The three-step pattern is online.
-
NEVER drop a column in the same deploy that stops using it.
Instead: deploy code that no longer references the column → wait for the deploy to settle → drop the column in a follow-up migration.
Why: during a rolling deploy, old pods still query the column; dropping it in the same release crashes those pods until they're replaced. Production goes down for the duration of the rollout.
-
NEVER add an index on a large table without CONCURRENTLY (Postgres).
Instead: hand-edit the migration SQL to use CREATE INDEX CONCURRENTLY and run it outside a transaction.
Why: a non-concurrent CREATE INDEX takes an ACCESS EXCLUSIVE lock — every read and write blocks for the duration. On a 50M-row table that's tens of minutes of full unavailability.
-
NEVER add a UNIQUE constraint without checking for existing duplicates.
Instead: run SELECT col, count(*) FROM t GROUP BY col HAVING count(*) > 1 against representative data first; resolve duplicates as a backfill.
Why: the migration will fail mid-deploy when it hits the first duplicate, leaving the schema in a partially-applied state — the worst kind of outage to recover from.
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NEVER assume drizzle-kit (or any ORM diff tool) generated what you intended.
Instead: read the generated SQL line by line. Renames are inferred as drop+add; default values can be reordered; cascades may differ.
Why: the schema is a model; the migration is data loss. The diff between them is where bugs live.
-
NEVER write a single migration that mixes additive, mutating, and destructive changes.
Instead: split per class. Even if the changes feel related, they have different rollout requirements.
Why: the safe rollout for a destructive change waits between steps; bundling it with additive changes either holds back the additive deploy or rushes the destructive one. Either is wrong.
-
NEVER drop or truncate data without an explicit user y and a recorded backup pointer.
Instead: confirm verbally; record the pre-migration snapshot SHA, dump file path, or PITR timestamp in a notes file alongside the migration.
Why: dropped data is unrecoverable from the migration alone. A recorded backup pointer is the only thing that turns the change from "permanent" into "reversible" if the deploy goes wrong.
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NEVER use DEFAULT now() (or DEFAULT random()) in a column-add that's expected to set a literal value at migration time.
Instead: decide explicitly: backfill with a single literal computed once, or accept that each row gets its own evaluation when the migration runs.
Why: DEFAULT now() evaluates per row at the time the row is rewritten — for a long ALTER TABLE, you get spread-out timestamps that look like real activity but aren't. The bug surfaces months later in audit log analyses.
