By tonone-ai
Analyzes slow SQL queries by reading execution plans, then recommends indexes and rewrites SQL to boost performance. Supports PostgreSQL, MySQL, SQLite, Prisma, and Drizzle.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin flux-queryAnalyze and optimize SQL queries for better performance, suggesting indexes, query rewrites, and execution plan improvements
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
SQL query optimization and execution plan analysis
SQL optimization, query tuning, and database performance expert for production systems
SQL query optimization for PostgreSQL/MySQL with indexing, EXPLAIN analysis. Use for slow queries, N+1 problems, missing indexes, or encountering sequential scans, OFFSET pagination, temp table spills, inefficient JOINs.
Database architecture and SQL optimization with PostgreSQL expertise
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".