By tonone-ai
Audits Redis caching implementations for missing TTLs, key collisions, stampede risks, and eviction policy mismatches, providing actionable recommendations to improve cache performance and reliability.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin cache-reconInfrastructure Specialist Team — Cache: Caching strategy — Redis/Memcached design, cache invalidation, eviction policies, application caching patterns
Optimize caching strategies for improved performance
Caching specialist for Redis patterns, Memcached, cache invalidation strategies, TTL management, cache-aside pattern, write-through caching, CDN integration, and HTTP caching headers. Use when implementing or optimizing caching strategies.
Challenge technical implementations before they ship. Rates solutions across correctness, completeness, scalability, security, and maintainability with real-world postmortem evidence.
Production-ready Redis integration for caching, sessions, rate limiting, pub/sub, and AI embedding cache with multi-framework support
Launch agent teams for any kind of work — coding, writing, diagnosis, and more
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".