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Convex Advisor

ConvexOfficialPublished by Convex

  • Skill
  • Review and audit
  • Software development
  • Apache-2.0
  • No issues found

From the author

Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.
— the description by Convex

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The convex-advisor performs a data-driven audit of Convex deployments by analyzing 72-hour health insights. It correlates runtime events—such as read limit hits and OCC contention—with specific code locations to provide evidence-backed findings. The skill focuses on performance, cost, and reliability, offering concrete fixes for identified bottlenecks while maintaining a strict read-only posture regarding production environments.

What it does

  • Retrieves and analyzes 72-hour deployment health insights including read limits and OCC contention.
  • Maps runtime insight events to specific lines of code to identify the root cause.
  • Categorizes findings by severity and impact, such as performance, cost, or correctness.
  • Emits structured findings to a central bus for further action or automated remediation.
  • Recommends specific architectural patterns like sharded counters or pagination to resolve bottlenecks.
  • Verifies the impact of applied fixes by monitoring trends in subsequent deployment insights.

When to use it

  • When investigating performance degradation or unexpected cost spikes in a Convex deployment.
  • During routine health checks to identify potential bottlenecks before they impact production.
  • When troubleshooting frequent OCC contention or read limit warnings.
  • As part of a launch-readiness assessment to ensure code efficiency.

Advantages

  • Evidence-based analysis derived from actual deployment runtime data.
  • Reduces manual debugging time by linking symptoms directly to code locations.
  • Promotes architectural best practices through recommended component-based fixes.
  • Maintains safety by operating in a strictly read-only mode for production environments.

Usage examples

“Analyze my production deployment for performance bottlenecks.”
The agent retrieves the 72-hour insights, identifies a read limit hit in the messages:list function, points to the unindexed filter in the code, and suggests adding an index.
“Why are my write operations failing with OCC contention?”
The agent inspects the OCC retry logs, identifies a shared counter causing contention, and recommends implementing a sharded counter component.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/convex/convex-advisor/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip convex-advisor.zip -d .agents/skills

Documentation by Convex

<!-- GENERATED from convex-agents content/capabilities/convex-advisor.json — do not edit by hand. -->

Live-deployment advisor

Static review guesses; the deployment KNOWS. The official Convex MCP ships an insights tool with typed 72h health events per function — documentsReadLimit / bytesReadLimit (hard limit hits), documentsReadThreshold / bytesReadThreshold (approaching), occFailedPermanently / occRetried (write contention) — each carrying evidence (table_name, bytes_read, documents_read, occ document id + retry count). The advisor turns each event into a root-caused finding by reading the flagged function's actual code, and emits findings on the findings bus (specs/finding.schema.json) so fixers can be dispatched and launch-readiness can score.

Workflow

  1. GUARD: run deploy-guard step 0-1 — identify + announce the deployment being read. Reading insights/logs on prod is allowed read-only; never enable mutating prod access for an advisory pass.

  2. GATHER (deterministic, via the official Convex MCP): status → deployment selector; insights → the typed 72h events; tables → schema + row counts; functionSpec → the public/internal surface. The insights tool is only available on cloud dev/prod deployments when logged in as a user (not on previews or deploy-key-scoped contexts) and needs ~72h of traffic; if it returns nothing or is unavailable, say so and fall back to offering convex-reviewer — do NOT invent findings.

  3. ROOT-CAUSE each insight event by reading the flagged function's code:

    • bytesReadThreshold/Limit or documentsReadThreshold/Limit → look for .collect() / unindexed .filter() / missing pagination on the named table; the fix is an index + .withIndex, .take(n), or .paginate (convex-expert patterns), or an aggregate component for counting shapes.

    • occRetried / occFailedPermanently → look for read-modify-write hotspots on the named document (shared counters, status toggles); the fix is @convex-dev/sharded-counter, narrowing the read set, or moving contention to a workpool.

    • repeated failures in logs (status: failure) → classify: crash loop in a cron, validator rejections, unhandled error shapes.

  4. EMIT findings per specs/finding.schema.json: class perf/correctness/cost, severity from the insight kind (limit hits = high, thresholds = med, retried = med, permanent OCC failure = high), locus {kind: deployment, functionId, tableName}, evidence {kind: insight-event, detail: the raw event}, confidence: confirmed (the event happened — it is not a hypothesis), fixCapability + autofixable where the repair is mechanical.

  5. REPORT: findings ranked by severity, each with (a) the runtime evidence in one line ('messages:list read 4.2MB from messages 31× yesterday'), (b) the code-level root cause with file:line, (c) the concrete fix and which capability applies it. Offer to apply fixes; apply only on confirmation, then re-run insights after traffic to verify the trend, or re-run the static check immediately.

  6. Scope discipline: this is a health/perf/cost pass. Route authz findings to convex-authz, code-idiom findings to convex-reviewer, error triage to sentinel — emit a pointer finding rather than duplicating their work.

Rules

  • Evidence-not-vibes: every finding cites a real insight event, log line, or table stat — if the deployment has no evidence, the advisor has no findings (offer convex-reviewer instead).

  • Read-only by construction: an advisory pass never mutates any deployment and never enables prod mutation flags (deploy-guard discipline applies).

  • Root-cause in the code before reporting: an insight event names the symptom; the finding must name the line and the mechanism.

  • Emit on the findings bus (specs/finding.schema.json), confidence: confirmed — runtime events are facts, not hypotheses.

  • Severity from the event kind: limit-hit / permanent-OCC-failure = high; threshold / retried = med.

  • Stay in lane: perf/cost/health only — hand authz to convex-authz, style to convex-reviewer, error triage to sentinel.

  • Prefer component fixes over hand-rolls when they match (sharded-counter for OCC on counters, aggregate for count scans) — same bias as suggest.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

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Attribution and licence

Published by
Convex
Publisher
Convex (Official)
Source
convex/agent-skills · skills/convex-advisor · commit f60629d
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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