How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill convexBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
Set up a project with two deployments, dev (break anything) and prod (real users), and never point local dev at prod data:
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 deployWrite every function so it is safe and cheap:
- Every public
query,mutationandactionis callable by anyone who has the deployment URL. Check the user (ctx.author the Better Auth helper) and ownership inside each one; useinternalQuery/internalMutationfor anything only the server calls. - Validate every argument with
v.*validators. - Read with
.withIndex(...)on a selective index, never.filteron a large table. Define the index inschema.tsfor 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:
npx convex insights --prod --detailsExtract 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/.collectwithout 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.




