melvynx/ai-maxxing-skills

convex

Set up, secure and optimize a Convex backend.

Ver código fuente

Cómo usar

  1. Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
  2. Consulta las herramientas, credenciales y entornos necesarios en el documento original.
  3. Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill convex

Antes de empezar

  • La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
  • Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

Set up a project with two deployments, dev (break anything) and prod (real users), and never point local dev at prod data:

bash
npx convex login                 # the user approves in the browser
npx convex dev --once            # creates the project + dev deployment, writes .env.local
npx convex env set KEY value     # server secrets (dev); add --prod for production
npx convex deploy                # production deploy

Write every function so it is safe and cheap:

  • Every public query, mutation and action is callable by anyone who has the deployment URL. Check the user (ctx.auth or the Better Auth helper) and ownership inside each one; use internalQuery/internalMutation for anything only the server calls.
  • Validate every argument with v.* validators.
  • Read with .withIndex(...) on a selective index, never .filter on a large table. Define the index in schema.ts for each access pattern (by_userId, by_courseId_order, ...).
  • Bound reads: .take(n) or .paginate() before enriching rows; never .collect() a table that grows.
  • Return only the fields the client needs.
  • Secrets live in Convex env vars, never in client code.

To optimize, start from live evidence:

bash
npx convex insights --prod --details

Extract function name, warning type, per-table docs/bytes, and whether it failed or only neared a limit. Prioritize high Documents Read with low Documents Returned.

Map each hot export to convex/** and its client callsites, then diagnose before patching:

  • .filter / .collect without a selective .withIndex, or an index that narrows only by tenant and still scans thousands of rows;
  • per-row related fetches (N+1); recount helpers on hot mutations;
  • jobs or crons that scan every row on every run;
  • concurrent patches on the same document (OCC conflicts).

Apply the smallest change: add the index used by the query; .take/paginate before enrichment; denormalize list previews and fetch full rows only on detail; incremental counters instead of full recounts; cursor + per-run limit for jobs; skip no-op patches and merge same-document writes; move high-churn state to a narrower table.

Verify with the project's typecheck, tests and build, plus npx convex codegen. Re-run insights after deploy and report: hot functions, root cause, change, expected read/byte/OCC impact, validation.

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