Convex Cost
ConvexOfficialPublished by Convex
- Skill
- Review and audit
- Cloud and DevOps
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides visibility into Convex application costs by analyzing function execution data. It correlates bytes and documents read with call volume to identify primary cost drivers. By projecting how these drivers scale as data accumulates, it helps developers understand the financial impact of their query patterns. The skill also enforces a confirm-cost protocol for any metered or paid actions, ensuring explicit user consent before proceeding with changes that incur charges.
What it does
- Ranks functions by their contribution to data-read costs based on call volume and read intensity.
- Projects the growth curve of cost drivers to show how expenses scale with data accumulation.
- Identifies the most cost-effective optimizations for specific query patterns.
- Enforces a mandatory confirmation step for any metered or paid actions.
- Provides relative cost guidance based on query shapes and deployment insights.
When to use it
- When you need to understand which functions are driving your Convex application spend.
- Before implementing changes that might impact data-read costs.
- When evaluating the long-term cost implications of current query patterns.
- Before executing any metered or paid actions within the platform.
Advantages
- Translates technical query patterns into clear cost-related insights.
- Helps prevent unexpected cost growth by highlighting inefficient read patterns.
- Provides actionable optimization paths for identified cost drivers.
- Ensures financial control through explicit confirmation requirements for paid actions.
Usage examples
- “Analyze my current Convex function costs.”
- The agent will rank your functions by read volume, project their growth curves, and suggest specific optimizations like indexing or pagination.
- “What is the cheapest way to fix the cost of my messages:list function?”
- The agent will identify the specific read inefficiency and recommend the most cost-effective fix, such as adding an index or using a different retrieval method.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/convex/convex-cost/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip convex-cost.zip -d .agents/skills
Documentation by Convex
<!-- GENERATED from convex-agents content/capabilities/convex-cost.json — do not edit by hand. -->
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:
insightsfor the bytes-read / documents-read events (the direct cost signal — Convex bills on function calls + bandwidth),tablesfor row counts (a table's size bounds its scan cost),functionSpecfor 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 +
.withIndexinstead of scan,.paginate/.takeinstead 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer846 B
- LICENSELicence file added by AutoGineer10.7 KiB
- SKILL.md4.0 KiB
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Attribution and licence
- Published by
- Convex
- Publisher
- Convex (Official)
- Source
- convex/agent-skills · skills/convex-cost · commit f60629d
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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