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Found 52 Skills
Optimize web performance using Core Web Vitals, modern patterns (View Transitions, Speculation Rules), and framework-specific techniques
Implement error tracking with Sentry for automatic exception monitoring, release tracking, and performance issues. Use when setting up error monitoring, tracking bugs in production, or analyzing application stability.
PostgreSQL 数据库管理
Monitor and optimize search performance with Google Search Console.
PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
Use this skill for proactive backend health audits in an InsForge project — security misconfigurations, performance regressions, and system health issues surfaced by `diagnose advisor`, plus the backend-side deep-dives that pair with each advisor issue. Also use this skill when a user reports backend-wide performance degradation (high CPU/memory, all responses slow, connection pool exhaustion, lock contention) without a single failing request. Trigger on requests like "health check", "audit my backend", "review security", "check RLS policies", "find slow queries", "backend performance review", "high CPU/memory", "everything is slow", "EC2/database/system health", or pre-launch readiness audits. For reactive runtime errors with a single concrete failing request (SDK error objects, HTTP 4xx/5xx, function failures, deploy failures), use `insforge-debug` instead.
Configure Sentry for error tracking, performance monitoring, and log aggregation. Integrates with Pino to forward logs to Sentry automatically.
Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or implementing evals. Triggers include "agent performance", "token usage", "latency optimization", "eval", "agent metrics", "cost optimization", "agent benchmarking".
Defines database performance monitoring strategy with slow query detection, resource usage alerts, query execution thresholds, and automated alerting. Use for "database monitoring", "performance alerts", "slow queries", or "DB metrics".
Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-technical founders.
MetricKit API reference for field diagnostics - MXMetricPayload, MXDiagnosticPayload, MXCallStackTree parsing, crash and hang collection