Agent skill · data analytics · aiskillstore
convex-performance-audit
Audits and optimizes Convex application performance across hot-path reads, write contention, subscription cost, and function limits. Use this skill when a Convex feature is slow or expensive, npx convex insights shows high bytes or documents read, OCC conflict errors or mutation retries appear, subscriptions or UI updates are costly, functions hit execution or transaction limits, or the user mentions performance, latency, read amplification, or invalidation problems in a Convex app.
Why this skill is useful
Provides specific performance optimization techniques and insights for Convex applications that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Convex Performance Audit Diagnose and fix performance problems in Convex applications, one problem class at a time. When to Use A Convex page or feature feels slow or expensive npx convex insights --details reports high bytes read, documents read, or OCC conflicts Low-freshness read paths are using reactivity where point-in-time reads would do OCC conflict errors or excessive mutation retries High subscription count or slow UI updates Functions approaching execution or transaction limits The same performance pattern needs fixing across sibling functions When Not to Use Initial Convex setup, auth setup, or component extraction Pure schema migrations with no performance goal One-off micro-optimizations without a user-visible or deployment-visible problem Guardrails Prefer simpler code when scale is small, traffic is modest, or the available signals are weak Do not recommend digest tables, document splitting, fetch-strategy changes, or migration-heavy rollouts unless there is a measured signal, a clearly unbounded path, or a known hot read/write path In Convex, a simple scan on a small table is often acceptable. Do not invent structural work just because a pattern is not ideal at large scale First Step: Gather Signals Start with the strongest signal available: 1. If deployment Health insights are already available from the user or the current context, treat them as a first-class source of performance signals. 2. If CLI insights are available, run npx convex insights --details. Use --prod, --preview-name, or --deployment-name when needed. If the local repo's Convex CLI is too old to support insights, try npx -y convex@latest insights --details before giving up. 3. If the repo already uses convex-doctor, you may treat its findings as hints. Do not require it, and do not treat it as the source of truth. 4. If runtime signals are unavailable, audit from code anyway, but keep the guardrails above in mind. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/convex-performance-audit