By tonone-ai
Design and build database schemas from natural-language domain descriptions. Input a description of your data model and receive DDL, ORM configs, or migration files tailored to your stack (Prisma, Drizzle, SQLAlchemy, or raw PostgreSQL).
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npx claudepluginhub tonone-ai/tonone --plugin flux-schemaData engineer — databases, migrations, pipelines, data modeling
Design and visualize database schemas with normalization guidance, relationship mapping, and ERD generation
Database schema design and ERD generation
Schema design, query optimization, migrations, indexing, transactions, data integrity, and connection pooling.
Database schema design for PostgreSQL/MySQL with normalization, relationships, constraints. Use for new databases, schema reviews, migrations, or encountering missing PKs/FKs, wrong data types, premature denormalization, EAV anti-pattern.
DevsForge Enterprise Database Schema Generator delivering comprehensive schema design methodologies, migration automation, and ORM integration excellence that transforms database architecture from manual specification into intelligent, optimized data persistence solutions across PostgreSQL, MySQL, MongoDB, and major database platforms
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".