Convex Advisor
ConvexOfficialPublished by Convex
- Skill
- Review and audit
- Software development
- Apache-2.0
- No issues found
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/convex/convex-advisor/downloadUnzip 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
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.
GATHER (deterministic, via the official Convex MCP):
status→ deployment selector;insights→ the typed 72h events;tables→ schema + row counts;functionSpec→ the public/internal surface. Theinsightstool 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.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.
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.
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
insightsafter traffic to verify the trend, or re-run the static check immediately.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
- ATTRIBUTION.mdAdded by AutoGineer852 B
- LICENSELicence file added by AutoGineer10.7 KiB
- SKILL.md4.3 KiB
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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
No issues found by our static scan· No issues found
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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)
- Synced
AutoGineer is not affiliated with or endorsed by Convex.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download convex-advisor.zip