convex-insights
Разобрать ошибки и состояние приложения Convex
Что получите
Краткий вывод с количеством событий, характерным примером ошибки или записи журнала и, когда возможно, ссылкой на соответствующий экран Convex Dashboard.
Что передать
Развёртывание Convex и вопрос; при необходимости — функция или идентификатор запроса.
Проверен состав файлов. Запуск не тестировался. Что проверено
Оценки пользователей
Как использовать
Помогает разработчикам работающего приложения Convex выяснить, какие вызовы завершаются ошибкой, почему не удался конкретный запрос и какие сигналы проблем есть у развёртывания. Выводы сопровождаются данными для проверки.
- Укажите, какое развёртывание нужно изучить и что вас интересует: ошибки, состояние или отдельный вызов.
- Для конкретного сбоя передайте известные функцию и идентификатор запроса.
- Если проверяете последствия обновления, попросите сопоставить начало ошибок с версией развёртывания.
Условия работы
- Используется официальный MCP Convex; работа ограничена чтением, без изменений и включения разрешений на изменение рабочей среды.
- Просмотр состояния доступен только для облачных сред разработки и рабочей среды с пользовательской авторизацией.
- Журналы выбираются по числу последних записей и фильтруются локально; совпадение с обновлением не доказывает причинную связь.
- Исправление кода, производительности и стоимости передаётся другим скиллам.
Пример запроса после установки
Разбери ошибки в указанном развёртывании Convex после последнего обновления. Сгруппируй их по функциям, покажи подтверждающие записи и ссылку для проверки.
Пример составлен по инструкции автора; результат запуска не проверялся.
Основание: инструкция закреплённой версииДля установки
Node.js, npm и Git для команды выше. Подключения и инструменты для работы скилла настраиваются отдельно.
Совместимые агенты
Совместимость указана для инструкций. Запуск в каждом приложении не тестировался; для веб-чата способ подключения может отличаться.
Файлы скилла
GitHubЧитать инструкцию SKILL.md
Исходный текст закреплённой версии.
--- name: convex-insights description: "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." --- <!-- GENERATED from convex-agents content/capabilities/convex-insights.json — do not edit by hand. --> # Query logs + health in natural language 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. ## Workflow 1. GUARD: deploy-guard step 0-1 — identify + announce which deployment is being read. Reading logs/insights is read-only; never enable prod mutation flags for an insights pass. 2. DISCOVER before you query — never guess identifiers. Use `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. 3. PICK ONE OF THREE VIEWS and fetch the raw window, then filter locally: - failures view → `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'. - health view → `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. - trace view → `logs --history <n> --jsonl` then locally filter to one requestId/function to read the full execution. Answers 'why did THIS call fail'. 4. SCOPE by fetching a bounded recent window (a sensible `--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. 5. ANSWER with (a) the one-line finding, (b) the evidence (counts + one representative stack/log line), and (c) WHEN POSSIBLE an agent-constructed dashboard deep link (dashboard.convex.dev, the deployment's Logs/Functions view) for human verification — no tool returns the link, so build it from the deployment name + function; never a raw log dump as the answer. 6. CROSS-CHECK deploy causality when asked 'did my deploy break this': compare the failure onset (from the log timestamps) against the deployment version from `status`; correlate, don't assert. 7. HAND OFF, don't fix here: a perf/cost cause → convex-advisor (which owns those fixes); a code defect → convex-reviewer/convex-authz; a live error to react to going forward → monitor/sentinel. Emit findings on the bus (specs/finding.schema.json) — primarily `observability`, with perf/cost as pointer findings to advisor — so a composite pass can pick them up. ## Rules - Discover real function/field names (functionSpec/status) before filtering — never guess identifiers, never return a confusing empty result for a name the app doesn't have. - `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. - One of three views per question (failures / health / trace) — don't fan out into many speculative tool calls. - No tool returns a dashboard link — construct it from the deployment name + function when possible for human verification; never answer with a raw log dump. - Read-only always: an insights pass runs no mutation and never enables prod mutation flags (deploy-guard discipline). - Stay a reader and defer perf/cost fixes to convex-advisor: emit primarily `observability`, route perf/cost as POINTER findings so advisor uniquely owns the perf-fix framing; forward-looking reaction goes to monitor/sentinel.
Проверка и особенности
Автоматическая проверка источника и файлов
Автоматически проверены публичный репозиторий, лицензия, заголовок SKILL.md, состав текстового пакета и Git-хеш каждого файла. Пакеты с исполняемыми файлами и срабатываниями применённых статических правил не включены. Ручной аудит всех инструкций и функциональный запуск не проводились. Внешние инструменты и сервисы не проверялись. Проверка не гарантирует безопасность.
В публичном списке установок на 2026-10-09 у скилла было 30 273 установок. Порог отбора — 1 000 установок. Число установок показывает распространённость, а не качество или безопасность инструкции.
- Проверка состава
- 09.10.2026
- Проверенная версия
2cfe645c87f9- Файлы с кодом
- Не указаны в данных проверки
- Изменения на GitHub
- Нужна повторная проверкаПоследняя проверка: 11.10.2026
Репозиторий и лицензия
Это показатели всего репозитория, а не установки скилла. Снимок требует обновления.
- Последняя активность GitHub
- 02.10.2026
- Лицензия
- Apache-2.0
- Добавлен в каталог
- 09.10.2026
Полный текст лицензии
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