Convex Cost Optimizer

SkillDatabases & data

Use when optimizing Convex database read bandwidth, documents-read cost, query indexes, full-table scans, function-level usage dashboard findings, or code that uses .filter(), unbounded .collect(), N+1 reads, or expensive reactive Convex queries.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Convex Cost Optimizer skill

What this skill tells your AI

The instructions your AI receives, as published by melvynx/saveit.now in .agents/skills/convex-cost-optimizer/SKILL.md and read by ahel’s review.

Reduce Convex database bandwidth and read amplification while preserving query correctness. This skill is narrower than convex-performance-audit: use it when the signal is specifically high database read bandwidth, high documents read, full-table scans, index design, or expensive query syntax.

Required Context

  • Before editing convex/**/*.ts, read convex/_generated/ai/guidelines.md.
  • Read .agents/rules/convex.md and .agents/rules/development-commands.md before changing schema or Convex functions.
  • If the user has dashboard data, treat it as the strongest signal.
  • If no dashboard data is available, use npx convex insights --details when supported, or audit the code directly and state that the ranking is code-derived rather than usage-derived.

Dashboard Triage

Start from the Convex usage dashboard database bandwidth section:

  1. Sort by database read bandwidth or documents read.
  2. Open the function-level breakdown.
  3. Prioritize functions where "Documents Read" is much higher than "Documents Returned".
  4. Map each hot function name to the matching file under convex/.
  5. Inspect client callsites using useQuery, usePaginatedQuery, fetchAuthQuery, or route loaders so you understand call frequency.

A high documents-read/documents-returned ratio usually means a full-table scan, unbounded read, or read-after-filter pattern.

Code Audit Patterns

Search for these first:

rg -n "\\.filter\\(|\\.collect\\(\\)|collect\\(\\)\\.length|for \\(.*await ctx\\.db\\.get|ctx\\.db\\.query" convex

Flag these patterns:

  • ctx.db.query("table").filter(...)
  • ctx.db.query("table").collect() followed by JavaScript .filter(...)
  • collect().length for counts
  • unbounded .collect() on user-facing or reactive paths
  • ctx.db.get(...) inside loops when the loop can grow
  • repeated helper calls that each do their own query
  • list screens returning full documents when the UI only needs summaries
  • reactive useQuery data that only needs a point-in-time server read

Fix Order

Apply the smallest high-impact fix first.

  1. Replace full scans with indexes.
  2. Bound result sets with .take(n) or .paginate(...).
  3. Replace collect().length with maintained counters or aggregate documents.
  4. Batch foreign-key lookups and avoid N+1 query loops.
  5. Return smaller DTOs from hot list functions.
  6. Denormalize or introduce digest/summary tables only when measured cost, row size, or call frequency justifies the migration complexity.

When an index or denormalized field depends on old data being backfilled, use the migration helper workflow instead of assuming old documents have the new shape.

Index Syntax

Schema:

messages: defineTable({
  channelId: v.id("channels"),
  body: v.string(),
}).index("by_channelId", ["channelId"]),

Query:

const messages = await ctx.db
  .query("messages")
  .withIndex("by_channelId", (q) => q.eq("channelId", channelId))
  .take(50);

Use Convex index syntax exactly:

  • In schema, define .index("by_fieldName", ["fieldName"]).
  • For compound indexes, include all fields in the name, for example .index("by_organizationId_and_status", ["organizationId", "status"]).
  • In .withIndex(), compare indexed fields with q.eq("fieldName", value), not q.eq(q.field("fieldName"), value).
  • Put equality fields first in compound indexes. Add range or ordering fields only when the query needs them.
  • One compound index can serve a prefix query. Do not add both by_org and by_org_and_status unless the single-field index is needed for _creationTime ordering.
  • Use .withSearchIndex(...) for text search instead of scanning and filtering.

Correctness Checks

For every rewritten query, verify:

  • The new index returns the same records as the old filter.
  • Optional or newly-added fields do not exclude older documents unexpectedly.
  • Sorting remains correct after switching to the index.
  • Authorization checks still happen before returning data.
  • Pagination cursors and limits still match the UI contract.
  • Sibling functions over the same table are inspected for the same pattern.

Verification

Run the smallest checks that cover the change:

  • npx convex dev --once for schema and generated API changes.
  • cd web-app && npm run typecheck for TypeScript correctness.
  • cd mobile-app && npx tsc --noEmit when mobile imports generated APIs.
  • cd mobile-app && npm run lint when mobile style-sensitive files changed.
  • Re-check dashboard/insights after deploy when the user is validating real cost reduction; local checks prove syntax and behavior, not production bandwidth savings.

Update docs or release notes only when this repo already has a matching changelog/release-note convention for the touched area.

Signals

GitHub stars
31
Forks
5
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
convex-cost-optimizer
Source
github.com/melvynx/saveit.now