Schema Evolution Manager

376

a5c-ai

Manages schema evolution and compatibility across data systems

data-modeling

206

rsmdt

Schema design, entity relationships, normalization, and database patterns. Use when designing database schemas, modeling domain entities, deciding between normalized and denormalized structures, choosing between relational and NoSQL approaches, or planning schema migrations. Covers ER modeling, normal forms, and data evolution strategies.

API Schema Evolution Testing

71

PramodDutta

Testing API schema evolution patterns including additive changes, field deprecation, type widening, and backward-compatible migration strategies.

alembic

40

manutej

Comprehensive Alembic database migration management for customer support systems

data-modeling

31

melodic-software

Use when designing data models, database schemas, or choosing between modeling approaches. Covers dimensional modeling, star schema, data vault, entity-relationship design, and schema evolution.

dynamic-schema-design

31

melodic-software

Use when implementing flexible content schemas using EF Core JSON columns, `OwnsOne().ToJson()` patterns, or designing dynamic field storage that avoids migrations. Covers JSON column configuration, LINQ querying of JSON properties, indexing strategies, and schema evolution patterns for headless CMS architectures.

migration-specialist

29

omer-metin

Migration specialist for zero-downtime schema changes, data migrations, and backward-compatible evolution. Use when tags such as migration, schema change, database migration, zero downtime, backward compatible, rollback, blue-green, data migration, schema, database, feature-flag, ml-memory are mentioned.

data-validation

27

majesticlabs-dev

Data validation patterns and pipeline helpers. Custom validation functions, schema evolution, and test assertions.

parquet-coder

27

majesticlabs-dev

Columnar file patterns including partitioning, predicate pushdown, and schema evolution.

protobuf

8

way-platform

Use when working with Protocol Buffer (.proto) files, buf.yaml, buf.gen.yaml, or buf.lock. Covers proto design, buf CLI, gRPC/Connect services, protovalidate constraints, schema evolution, and troubleshooting lint/breaking errors.

scalable-data-schema

5

sunnypatneedi

Design database schemas that scale from prototype to production. Use when planning data models, designing tables, optimizing queries, or migrating schemas. Covers normalization, indexing, partitioning, and evolution strategies for SQL and NoSQL databases.

data-intensive-patterns

5

ZLStas

Generate and review data-intensive application code using patterns from Martin Kleppmann's "Designing Data-Intensive Applications." Use this skill whenever the user asks about data storage engines, replication, partitioning, transactions, distributed systems, batch or stream processing, encoding/serialization, consistency models, consensus, event sourcing, CQRS, change data capture, or anything related to building reliable, scalable, and maintainable data systems. Trigger on phrases like "data-intensive", "replication", "partitioning", "sharding", "LSM-tree", "B-tree", "transaction isolation", "distributed consensus", "stream processing", "batch processing", "event sourcing", "CQRS", "CDC", "change data capture", "serialization format", "schema evolution", "consensus algorithm", "leader election", "total order broadcast", or "data pipeline."

data-intensive-patterns

5

booklib-ai

Generate and review data-intensive application code using patterns from Martin Kleppmann's "Designing Data-Intensive Applications." Use this skill whenever the user asks about data storage engines, replication, partitioning, transactions, distributed systems, batch or stream processing, encoding/serialization, consistency models, consensus, event sourcing, CQRS, change data capture, or anything related to building reliable, scalable, and maintainable data systems. Trigger on phrases like "data-intensive", "replication", "partitioning", "sharding", "LSM-tree", "B-tree", "transaction isolation", "distributed consensus", "stream processing", "batch processing", "event sourcing", "CQRS", "CDC", "change data capture", "serialization format", "schema evolution", "consensus algorithm", "leader election", "total order broadcast", or "data pipeline."

clickhouse-cdc

4

dandedotdev

Use when syncing data FROM relational databases (PostgreSQL, MySQL, MongoDB) TO ClickHouse. Covers change data capture using Debezium, Airbyte, or custom triggers. Includes handling schema evolution, DELETE operations, and maintaining consistency. NOT for message queues (see clickhouse-streaming) or query optimization (see clickhouse-patterns).

zod-contract-testing

4

apankov1

Validates Zod schema parsing at boundaries. Tests valid/invalid inputs, schema evolution, refinement coverage, and compound state matrices (2^N optional field combinations).

design-serialization-schema

2

pjt222

Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

design-serialization-schema

2

pjt222

Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

database schema and migrations

IronSyd

Plan database schema evolution, migration rollout, backfills, and rollback. Use when introducing schema changes, data transformations, or compatibility-sensitive DB updates.