Structured code review through the shared OpenClaw agent-skills installation.
日本語の概要は準備中です。原文の説明を表示しています。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
The deployment already records what happened; the agent just has to ask well. This capability is a disciplined wrapper over the official Convex MCP's read tools (logs, insights, functionSpec, status) that turns operational questions into narrow, evidence-returning queries and hands back answers a human can one-click verify in the dashboard. The discipline is copied from the observability MCP surface that works best in the wild: discover fields before querying, three views not fifteen tools, token-frugal output, and a dashboard deep link on every answer.
functionSpec to list the real function names and status for the deployment/version. Note the tool limits up front: logs takes only --history <n> (a COUNT, not a time window), --success, --jsonl, --prod, --deployment — there is NO server-side status/function/requestId/time filter; insights has no function filter and is cloud dev/prod + user-auth only. So you fetch a recent window and filter CLIENT-SIDE.logs --history <n> --jsonl, then locally keep failures + group by function + error message, returning counts + the first stack per group. Answers 'what's erroring', 'what failed after deploy'.insights (cloud only): the typed 72h read-limit / OCC events. Surface + rank them, but hand perf/cost ROOT-CAUSING and fixes to convex-advisor — emit those as pointer findings, do not own the perf-fix framing here.logs --history <n> --jsonl then locally filter to one requestId/function to read the full execution. Answers 'why did THIS call fail'.--history count) and filtering client-side to the function/status/requestId asked about; when the window is large, aggregate (counts by function/message) rather than dumping lines.status; correlate, don't assert.observability, with perf/cost as pointer findings to advisor — so a composite pass can pick them up.logs and insights have NO server-side status/function/requestId/time-window filter (logs takes only a --history COUNT; insights is cloud-only) — fetch a bounded recent window and filter CLIENT-SIDE; say so rather than implying params that don't exist.observability, route perf/cost as POINTER findings so advisor uniquely owns the perf-fix framing; forward-looking reaction goes to monitor/sentinel.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Structured code review through the shared OpenClaw agent-skills installation.
日本語の概要は準備中です。原文の説明を表示しています。
Create and manage Axiom monitors and notifiers via the v2 public API. Use when building alerting, routing notifications, validating monitor behavior, and maintaining alert configurations end-to-end.
日本語の概要は準備中です。原文の説明を表示しています。
Expert SRE investigator for incidents and debugging. Uses hypothesis-driven methodology and systematic triage. Can query Axiom observability when available. Use for incident response, root cause analysis, production debugging, or log investigation.
日本語の概要は準備中です。原文の説明を表示しています。
Designs and builds Axiom dashboards via API. Covers chart types, APL and metrics/MPL query patterns, SmartFilters, layout, and configuration options. Use when creating dashboards, migrating from Splunk, or configuring chart options.
日本語の概要は準備中です。原文の説明を表示しています。
Use when drafting, sending, or preserving email correspondence for an existing ClawHub content rights case.
日本語の概要は準備中です。原文の説明を表示しています。
Apply ClawHub-specific Convex conventions and route to the right managed Convex skill. Use for any change under convex/, Convex commands or deployment targeting, query performance, migrations, retention, runtime validation, or skill stat reads and writes in the ClawHub repository.
日本語の概要は準備中です。原文の説明を表示しています。