By tonone-ai
Benchmark API latency and throughput across versions, detect performance regressions with p50/p95/p99 analysis, and get go/no-go recommendations for CI gates.
Compare API performance across versions — regression detection and root cause analysis.
Design a performance benchmark for an API — test scenarios, metrics, and tooling.
Audit existing performance testing — find missing benchmarks, stale baselines, and CI gaps.
Uses power tools
Uses Bash, Write, or Edit tools
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin benchDesign a performance benchmark for an API
API endpoint benchmarking and performance reporting
Use this agent for comprehensive performance testing, profiling, and optimization recommendations. This agent specializes in measuring speed, identifying bottlenecks, and providing actionable optimization strategies for applications. Examples:\n\n<example>\nContext: Application speed testing
Use this agent for comprehensive API testing including performance testing, load testing, and contract testing. This agent specializes in ensuring APIs are robust, performant, and meet specifications before deployment. Examples:\n\n<example>\nContext: Testing API performance under load
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".
Load testing and performance benchmarking with metrics analysis and bottleneck identification
API design toolkit for REST and GraphQL with endpoint design, versioning strategies, and OpenAPI documentation generation. Includes api architect agent for strategic API decisions.
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