convex-cost

Original：🇺🇸 English

Translated

Preview Convex spend — rank functions by bytes/documents-read × call-volume from insights, project each cost driver's growth curve, name the cheapest fix; confirm-cost for paid actions.

450installs

Sourceget-convex/agent-skills

Added on2026-08-02

NPX Install

npx skill4agent add get-convex/agent-skills convex-cost

SKILL.md Content

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Preview what this app will cost

Cost surprises come from a handful of functions reading far more data than anyone realized — the same read-heavy patterns convex-advisor flags for perf, seen through the money lens. This capability makes spend legible: it reads the deployment's own bytes/documents-read evidence, attributes it to the functions driving it, projects how it grows with traffic, and names the cheapest fix. It also carries the confirm-cost discipline (Supabase's structural consent for paid actions): before anything metered, state the price and get an explicit yes.

Workflow

GUARD: deploy-guard — a cost read is read-only over dev/prod (insights is cloud+user-auth only; not previews). Announce the deployment.
GATHER the spend evidence via the official MCP:
```
insights
```
for the bytes-read / documents-read events (the direct cost signal — Convex bills on function calls + bandwidth),
```
tables
```
for row counts (a table's size bounds its scan cost),
```
functionSpec
```
for the surface. If there's no usage/traffic yet, say so and estimate from the query SHAPES instead (a
```
.collect()
```
on a table projected to grow is a future cost even with zero traffic today).
ATTRIBUTE: rank functions by bytes/documents read per call × observed (or asked-about) call volume — the product is the cost driver, not either alone. A cheap-per-call function called constantly can outweigh an expensive rare one; show both factors.
PROJECT: state how the top drivers scale — a full-table
```
.collect()
```
grows LINEARLY with the table (cost compounds as data accumulates); an indexed
```
.take(n)
```
stays flat. Give the user the shape of the curve ('this is O(table size) per call — fine at 1k rows, a bill at 1M'), not a false-precision dollar figure.
NAME THE CHEAPEST FIX per driver — index +
```
.withIndex
```
instead of scan,
```
.paginate
```
/
```
.take
```
instead of
```
.collect
```
, an aggregate component for counts, caching a hot read — and emit it as a cost-class finding on the bus (evidence: the insight event + the projected growth) pointing at convex-expert/convex-advisor for the actual change.
CONFIRM-COST for paid actions: if the flow includes anything metered (a domain purchase, cloud provisioning, a plan change), STATE the price and recurrence explicitly and get an explicit yes BEFORE proceeding — never let a paid action happen as a side effect (the cost-confirm gate).
REPORT: the current cost drivers ranked, each with its evidence + growth shape + fix, and a plain bottom line ('your spend is dominated by messages:list reading the whole table every call; index it and it drops ~100x'). Honest precision: Convex pricing changes and depends on plan — give relative/shape guidance and cite the pricing page for absolute numbers rather than inventing a dollar total.

Rules

Cost = data-read-per-call × call-volume — always show both factors; a cheap function called constantly can cost more than an expensive rare one.
Read the deployment's own insights/bytes-read evidence for spend; with no traffic yet, price the query SHAPES (a scan on a growing table is a future cost).
Give the growth CURVE, not false-precision dollars: O(table) scans compound as data accumulates; indexed access stays flat. Cite the pricing page for absolute figures.
Every cost driver names its cheapest fix and emits a cost-class finding on the bus pointing at the fixer (convex-expert/advisor).
Confirm-cost for any metered/paid action: state the price + recurrence and get an explicit yes BEFORE it happens — never as a side effect.
Read-only over dev/prod (deploy-guard); insights is cloud+user-auth only. Cost composes convex-advisor's evidence but frames it as money, not latency.