convex-performance-audit

Audit Convex hot-path reads, write contention, and subscription costs.

32|1|Updated Jan 17, 2026

Search Tags:#performance #database #optimization #audit #latency #convex

Ready to run

npx skills add https://github.com/damien-schneider/reflet --skill convex-performance-audit-damien-schneider

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: convex-performance-audit
Source: https://github.com/damien-schneider/reflet/tree/main/packages/backend/.agents/skills/convex-performance-audit
Command: npx skills add https://github.com/damien-schneider/reflet --skill convex-performance-audit-damien-schneider

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses performance bottlenecks, high costs, and reliability issues in Convex applications, such as slow read paths, write contention, and excessive subscription overhead.

Core Features & Use Cases

Performance Diagnosis: Analyzes read/write patterns, OCC conflicts, and subscription costs using CLI insights and code audits.
Optimization Strategies: Provides actionable patterns for index usage, denormalization, batching mutations, and managing function budgets.
Use Case: If your application experiences frequent OCC conflict errors or high latency during peak traffic, this skill guides you through tracing the hot path and applying targeted fixes to reduce transaction work.

Quick Start

Use the convex-performance-audit skill to analyze the current performance signals and identify the primary bottleneck in the application.

Frequently Asked Questions about convex-performance-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I diagnose high latency and OCC conflict errors in my Convex application?▼

Diagnose Convex application latency and OCC conflict errors by auditing hot-path reads, write contention, and subscription costs using CLI insights and code analysis to identify primary bottlenecks.

What causes high read amplification in Convex and how can I reduce it?▼

High read amplification in Convex stems from inefficient transaction management and subscription overhead. Reduce it by applying index optimization, denormalization, and targeted fixes to lower transaction work.

What is the best way to optimize Convex subscription costs for production applications?▼

Optimize Convex subscription costs by analyzing reactive query patterns and applying actionable strategies like batching mutations, denormalization, and managing function budgets to reduce overhead.

Can I use this performance audit approach for a production-grade Convex project experiencing write contention?▼

Yes, this performance audit approach specifically targets production-grade Convex projects experiencing write contention by tracing hot paths and applying targeted fixes to reduce transaction work.

Why does my Convex app experience slow read paths during peak traffic?▼

Convex apps experience slow read paths during peak traffic due to read amplification and excessive subscription overhead. Trace the hot path and apply index optimization and batching mutations to resolve it.

When should I not use denormalization to fix Convex performance bottlenecks?▼

Avoid denormalization for Convex performance bottlenecks when the issue stems from write contention or OCC conflicts rather than read amplification, as it may increase transaction work without reducing latency.