Review 72 hours of Convex deployment health events and trace read limits or conflicting writes to the responsible code. Receive prioritized performance, cost, and reliability findings with runtime evidence, source locations, and concrete fixes.
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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.
安装前可以查看交给 AI 代理的指令内容。
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.
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..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.logs (status: failure) → classify: crash loop in a cron, validator rejections, unhandled error shapes.insights after traffic to verify the trend, or re-run the static check immediately.还没有评价。欢迎分享你的使用感受。
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Review code using shared OpenClaw instructions and a Python helper, while also following the target repository’s review requirements. This skill points to a common installation that supplies the actual review procedure.
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Create and manage Axiom alert rules and notification destinations through its API. Connect alerts to email, Slack, or webhooks, check monitor history, and adjust thresholds and evaluation schedules to address excessive or missing alerts.
中文简介正在准备中。当前显示英语简介。
Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.
中文简介正在准备中。当前显示英语简介。
Design and build Axiom dashboards from logs or metrics, the numerical measurements of system behavior. Create charts, filters, and layouts for monitoring and reporting, or migrate existing Splunk and Grafana panels while validating queries against available data.
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Drafts, previews, sends, and records emails for existing ClawHub content rights cases. It supports publisher removal notices and requester updates, requires approval of the final draft, and preserves the exact correspondence in the case record.
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Apply ClawHub-specific rules when developing with Convex, including deployment targeting, runtime checks, data migrations, search performance, and skill statistics. It also directs specialized work to the appropriate companion skills.
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