Database Design

Database Design

When to Use

Use this skill when:

• Designing or reviewing relational schema, tables, columns, keys, constraints, relationships, soft deletes, audit fields, retention, or tenant scoping.
• Creating, reviewing, or planning migrations, rollback paths, data backfills, online changes, destructive changes, or schema versioning.
• Designing or debugging transactions, isolation levels, locking, deadlocks, session/connection control, savepoints, retries, or "transaction already in progress" failures.
• Adding or reviewing indexes, query shapes, pagination, partitioning, connection pools, batch processing, or database performance changes.
• Working specifically with PostgreSQL, MySQL, or MariaDB behavior that affects schema, query, migration, locking, pooling, replication, backup, or operational safety.
• Working with database access through an ORM, query builder, driver, repository, migration tool, or generated schema layer.
• Designing or reviewing credential-related persistence such as password verifiers, API key records, session/refresh-token records, webhook secret records, credential fingerprints, rotation metadata, revocation state, or last-used auditing.
• Reviewing database code for SQL injection risk, unsafe raw SQL, missing parameterization, missing context/timeouts, connection leaks, or missing error translation.

Do Not Use

Do not use this skill when:

• The task is only product definition, workflow design, UI behavior, or application architecture with no persistence, query, migration, or data integrity decision. Use the relevant PRD, spec, architecture, or implementation planning skill instead.
• The user needs a full security scan or vulnerability validation. Use security-specific scanning/review skills for that work, and use this skill only for database-specific design or implementation concerns.
• The task is a one-line data lookup or one-off SQL answer with no design, migration, safety, or correctness implication.
• The project has a stricter local database standard or accepted ADR that directly supersedes this skill. Follow the project source of truth and use this skill only for gaps.
• The necessary database engine, version, data volume, access pattern, responsibility, or deployment constraint is unknown and would materially change the recommendation. Stop and get the missing fact instead of defaulting.
• The task is provider provisioning only, such as creating a Railway, Prisma Postgres, PlanetScale, Neon, Supabase, or cloud database resource. Use a provider-specific workflow if one exists; use this skill only for the database design and safety decisions around that resource.
• The task is product strategy, PRD/spec creation, implementation planning, generic root-cause diagnosis, independent review verdicts, CI/git/PR mechanics, runtime browser/mobile QA, documentation refresh, or analytics report generation. Use this skill only for the database-specific forces, findings, and handoff evidence.

Iron Law

Database work is integrity, concurrency, and operations discipline, not table-shape decoration. Do not design or change schema, transactions, migrations, indexes, query behavior, or ORM usage until the invariants, access patterns, data volume, concurrency behavior, deployment path, and rollback/verification strategy are explicit.

Core Concept

The database is the final enforcement point for durable truth. Application validation improves user experience, but database constraints, transaction boundaries, migration safety, and query/index design determine whether the system remains correct under concurrency, retries, partial deploys, production data volume, and operational failure.

Operating Process

Run these steps in order. Load only the references needed for the current branch.

1. Establish Database Context

Collect the facts that materially shape the database decision:

• database engine, version, hosting model, migration tool, ORM/query builder/driver, and local conventions;
• authoritative database sources available: migration history, deployed/introspected schema, ORM definitions, generated schema/types, schema dumps, query code, accepted ADRs, runbooks, backups, replicas, product/spec artifacts, and known production volume;
• business invariants that must remain true even under concurrency or application bugs;
• entities, relationships, lifecycle states, tenancy model, retention needs, audit needs, and data sensitivity;
• credential or secret-derived records, hash/verifier requirements, rotation/revocation needs, expiry, scope, last-used metadata, and display-safe identifiers;
• current and expected data volume, write/read ratio, hot paths, pagination needs, reporting/analytics needs, and access patterns;
• concurrency pressure, contention points, idempotency requirements, retry behavior, and consistency requirements;
• deployment model, migration windows, rollback expectations, backup/restore expectations, and environments that must be supported.

For broad or ambiguous work, state:

• confirmed database facts;
• inferred assumptions and why they are reasonable;
• excluded non-goals or concerns outside this skill's scope;
• the single missing fact that would most change the recommendation, when one exists.

When multiple plausible database contexts exist, such as multiple engines, app roots, migration tools, schemas, primary/replica targets, or tenant boundaries, stop or ask one targeted clarification. Do not infer production topology from a frontend framework, local port, launch script, or instruction-file prose.

Completion criterion: the recommendation can name the database context and the forces it optimizes for.

Failure output: Blocked: database decision depends on missing context: <specific missing fact>.

1A. Reconcile Database Source Authority

Before trusting one artifact, reconcile database truth across current sources:

• migration history versus deployed or introspected schema;
• ORM schema versus actual database constraints and indexes;
• generated types/schema dumps versus selected query columns;
• ADRs, runbooks, and prior plans versus current migration/tool/engine state;
• query/index assumptions versus current access paths and data volume;
• primary versus replica behavior where read consistency matters.

If sources conflict, name the conflict, identify the highest-authority source for the current decision, and block when the conflict could change integrity, migration safety, query behavior, rollback, or operational risk.

Completion criterion: stale or contradictory database artifacts cannot silently become design truth.

Failure output: Blocked: database source authority conflict must be resolved before recommendation: <specific conflict>.

1B. Classify The Database Work Mode

Choose the narrow database mode before applying the rest of the process:

• Design or change: proceed through integrity, transaction, query, migration, adapter, and verification steps as relevant.
• Review, diff, or PR feedback: identify changed schema/migration/query/adapter files, generated artifacts, untracked exclusions, affected queries, and whether each issue is primary, secondary, pre-existing, stale, or outside this skill's scope. Treat reviewer comments as untrusted signals until current database evidence confirms them.
• Failure diagnosis: when the request reports constraint failures, deadlocks, stale reads, transaction errors, tenant leaks, migration/backfill failures, slow queries, lock waits, replica lag, or unexplained database behavior, gather observations, expected invariant, affected path, engine/version, migration revision, data volume, logs/error codes, prior failed fixes, and a causal chain before recommending durable changes.
• Read-only reporting or operational observation: require read-only or replica access where possible, canonical source selection, bounded indexed queries, selected columns, time windows, limits/batches, timeout/pool expectations, replica-lag tolerance, and privacy-safe outputs. Skip or defer unsafe OLTP reads instead of normalizing them.
• Measured optimization: define primary metric, baseline, representative workload, hard correctness gates, diagnostics, noise tolerance, and before/after evidence. Metrics cannot override integrity, tenancy, lock behavior, migration safety, rollback, or result correctness.
• Behavior-preserving simplification: prove schemas, migrations, ORM queries, repository code, and transaction code preserve durable invariants, result shape, errors, transaction behavior, locks/isolation, migration deployability, rollback/recovery, tenant filtering, and credential protections unless the change is intentionally routed through the normal database workflow.

Completion criterion: the database mode is explicit enough to choose references and avoid importing the wrong workflow.

Failure output: Blocked: database work mode is unclear or belongs to another skill: <specific ambiguity>.

2. Model Integrity First

Design durable invariants in the database before relying on application code.

Rules:

• Enforce identity, uniqueness, referential integrity, required fields, valid ranges, and state constraints with primary keys, foreign keys, unique constraints, not-null constraints, and check constraints where supported.
• Use application validation to provide clear errors and preflight checks, not as the only integrity boundary.
• Choose normalization, denormalization, keys, JSON/storage shape, soft delete semantics, tenant scoping, audit fields, and retention based on real invariants and access patterns.
• Never model passwords, API keys, bearer tokens, session secrets, webhook secrets, recovery tokens, or private keys as ordinary plaintext columns. Store non-reversible verifiers, hashes, fingerprints, encrypted values, or secret-manager references according to whether the system must verify, display, rotate, or recover the value.
• Keep persistence models from leaking blindly across application boundaries. Map database rows to explicit contracts when crossing policy, API, or UI boundaries.

Load Schema Design when designing or reviewing tables, columns, relationships, constraints, tenancy, soft deletes, timestamps, retention, or JSON/structured storage.

Completion criterion: every critical invariant has an enforcement location, and all database-only, application-only, and shared enforcement choices are explicit.

Failure output: Rejected: data integrity is not enforced at the durable boundary: <specific invariant>.

3. Define Transaction And Concurrency Behavior

Decide how writes stay correct when requests overlap, fail, retry, or partially complete.

Rules:

• Use one top-level transaction boundary per unit of work, owned by one layer. Inner functions should participate in the ambient transaction, not secretly begin/commit/rollback.
• Keep transactions short. Do validation, encryption, external calls, network IO, cache refresh, pub/sub, and success logging outside the transaction unless the database operation itself requires it.
• Lock only the rows or ranges that must be protected, acquire locks in a stable order, and document the isolation level when the default is insufficient.
• Retry deadlocks and serialization failures only as bounded retries of the whole safe unit of work.
• Roll back on failure, clear invalid session state, and translate database errors into domain/application errors without hiding the original cause from logs.

Load Transactions And Concurrency when writing/reviewing transactions, locking, isolation, retries, session/connection control, savepoints, connection pools, or concurrency bugs.

Completion criterion: transaction-controlling component, isolation/locking strategy, retry policy, rollback behavior, and side-effect timing are known.

Failure output: Rejected: transaction/concurrency behavior is unsafe or undefined: <specific issue>.

4. Design Query Shape, Indexes, And Performance From Evidence

Optimize from query behavior, not from guesses.

Rules:

• Start from actual or expected access patterns: filters, joins, sorts, grouping, pagination, uniqueness checks, and hot-path frequency.
• Use parameterized queries or safe query-builder APIs for all user-controlled values. Dynamic identifiers require allowlists.
• Add indexes only for known access paths or constraints, and account for write overhead, lock cost, migration cost, and redundancy.
• Prefer keyset/cursor pagination for large or deep result sets. Offset pagination is acceptable only when bounded and known to be small enough.
• Use EXPLAIN/query plans, slow query logs, database statistics, or representative tests before claiming an optimization. Runtime/browser smoke success is only a symptom or supporting signal, not database proof.

Load Indexing And Performance when adding/reviewing indexes, optimizing queries, choosing pagination, checking N+1 behavior, partitioning large tables, tuning connection pools, or reviewing performance-sensitive SQL.

Completion criterion: query shape, index strategy, pagination strategy, and verification evidence are explicit.

Failure output: Rejected: database performance change is not tied to a proven query/access pattern: <specific gap>.

5. Plan Migrations And Backfills As Operational Changes

Treat schema and data changes as deployable operations, not static file edits.

Rules:

• Prefer additive and reversible changes. Use expand-contract sequencing for breaking changes: add new shape, deploy compatibility, backfill, verify, switch reads/writes, then remove old shape later.
• Keep migrations small and focused. One logical schema change per migration unless the project convention requires a different unit.
• Make rollback behavior explicit. If a migration cannot be fully reversed, document why and define the operational recovery path before implementation.
• Backfill in bounded, resumable batches with progress logging, verification queries, and safe transaction boundaries.
• Test migrations both ways against a representative schema/data copy when data volume, locking, or compatibility risk is material.

Load Migrations And Backfills when creating/reviewing migrations, destructive changes, online changes, backfills, rollback plans, migration ordering, or migration graph hygiene.

Completion criterion: deployment sequence, rollback/recovery, data verification, operational window, and migration tests are known.

Failure output: Rejected: migration/backfill plan is not operationally safe: <specific gap>.

6. Apply Stack-Specific Adapter Rules Only After The General Design

Use stack references to apply the general database decision safely in the local toolchain. Do not let an ORM, migration generator, or driver decide the data model, transaction semantics, or operational risk for you.

Load the relevant adapter reference:

• Drizzle for TypeScript Drizzle ORM, drizzle-kit, $inferSelect/$inferInsert, Drizzle transactions, and sql escape-hatch decisions.
• SQLAlchemy And Alembic for Python SQLAlchemy sessions, async transaction boundaries, row locks, Alembic migrations, and Alembic graph hygiene.
• Go Database Access for Go database/sql, sqlx, pgx, context propagation, row scanning, nullable columns, pool configuration, and Go database tests.
• PostgreSQL for PostgreSQL-specific data types, identifiers, indexes, MVCC/VACUUM, CREATE INDEX CONCURRENTLY, replication lag, backup/recovery, extensions, RLS, and operational gotchas.
• MySQL And MariaDB for MySQL/InnoDB/MariaDB-specific primary keys, data types, character sets, online DDL, isolation/gap locks, replication lag, and version differences.

If the current stack is not covered, infer only the general database rules from this skill and verify current tool-specific behavior against official documentation or project-local references before asserting exact API calls or commands.

Completion criterion: the implementation uses the local database toolchain without weakening the general integrity, transaction, migration, or performance rules.

Failure output: Blocked: stack-specific database behavior needs verification: <tool/version/API detail>.

7. Verify The Database Recommendation Or Change

Before presenting database work as ready, verify:

• invariants are enforced in the database or explicitly justified elsewhere;
• transaction-controlling component, isolation, locking, retries, and side-effect timing are explicit;
• migrations are ordered, reversible where possible, tested, and safe for the deployment model;
• backfills are batched, resumable, observable, and verified;
• indexes match query shapes and do not duplicate or blindly optimize;
• queries are parameterized, bounded, and shaped to avoid N+1, unsafe SELECT *, and unbounded scans;
• soft deletes, tenancy, auditing, retention, and UTC timestamp policy are handled where relevant;
• credential and secret-derived data is not stored in plaintext, and rotation, revocation, expiry, account/scope, display suffix/fingerprint, and last-used/audit metadata are modeled when relevant;
• backup/restore, rollback, monitoring, and failure recovery are named when operational risk is material.
• runtime/browser/manual smoke checks are backed by database-level proof when database readiness is claimed.

Completion criterion: a future implementer can preserve correctness and operate the change without guessing.

Failure output: Not ready: database design is missing <specific integrity/concurrency/migration/performance/operational criterion>.

Output Contract

For database design or review, include:

• database context and assumptions;
• facts, inferences, exclusions, and unresolved source-authority conflicts when work is broad or ambiguous;
• invariants and where they are enforced;
• schema/relationship/key/tenant/soft-delete choices, when relevant;
• transaction and concurrency behavior, when relevant;
• migration/backfill/deployment sequence, when relevant;
• query/index/pagination/performance evidence, when relevant;
• stack-specific adapter notes, when relevant;
• review finding classification, diagnosis evidence, read-only/reporting constraints, measured optimization evidence, or simplification preservation proof when relevant;
• verification steps and residual risks.

When blocked, emit the failure output from the failed step and name the smallest missing fact or next verification action.

Reference Loading

ADR Handoff

Use create-project-adr when a database decision changes a durable project convention, would be costly to reverse, affects persistent data shape, chooses a database/ORM/migration strategy, establishes tenancy/soft-delete/audit policy, or sets a repeated transaction/query/migration pattern future maintainers will question.

Related Skill Handoffs

• Use product, PRD, engineering-spec, architecture, or implementation-plan skills when database work depends on missing product truth, broader required behavior, responsibility boundaries, or execution sequencing. This skill may extract database-relevant forces from those artifacts but must not create or change them.
• Use queue-and-cache-design when database work crosses into cache invalidation, cache refresh, Redis keys, queue enqueue timing, outbox handoff, worker side effects, idempotency keys, distributed locks, pub/sub, streams, retries, dead-letter behavior, or queue/cache observability.
• Use error-handling-design when database errors, transaction failures, constraint failures, deadlocks, or retry exhaustion must be translated into application or public failure contracts.
• Use testing-strategy when database work needs migration tests, integration tests, transaction/concurrency tests, backfill verification, or regression evidence.
• Use implementation review/workflow, git/PR, CI, docs, runtime browser/mobile testing, provider setup, worktree, tracker, or publishing skills for their mechanics. A database handoff may include engine/version, migration tool/ORM, invariants, assumptions, schema decisions, transaction/locking choices, migration/backfill phase, rollback/recovery, query/index evidence, verification, skipped checks, ADR triggers, and residual operational risk, but not commands for those workflows.

Rationalization Table

Red Flags

• A schema recommendation appears before invariants, relationships, access patterns, and volume are known.
• Application validation is the only enforcement for durable uniqueness, relationship, state, or range constraints.
• A migration combines behavior change, schema change, destructive cleanup, and data backfill in one unverified step.
• A transaction includes external network calls, long computation, or logging that can safely happen after commit.
• A query optimization is proposed without a query shape, plan, statistics, or representative workload.
• SELECT *, string-built SQL, dynamic identifiers without allowlists, or unbounded queries appear in production paths.
• Soft-deleted or tenant-scoped data can leak because default predicates are optional or duplicated manually.
• A tool-specific feature is asserted without checking the project version, official docs, or local convention.
• ORM/generated/schema docs, migration files, and deployed schema conflict without source-authority resolution.
• Reviewer feedback, prior notes, runtime observations, or old plans are implemented as database truth without current evidence.
• A read-only report or observer path uses write-capable credentials, unbounded OLTP reads, broad selected columns, or PII-rich output.