melvynx/ai-maxxing-skills

convex

Set up, secure and optimize a Convex backend.

Voir la source

Mode d’emploi

  1. Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
  2. Consultez le document original pour les outils, identifiants et environnements requis.
  3. Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill convex

Avant de commencer

  • L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
  • Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

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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