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Found 551 Skills
Design and run a monitoring system for a website or web app. Use this skill when setting up uptime checks, defining SLOs, configuring error tracking, choosing what to alert on, designing on-call rotations, or fixing alert fatigue. Triggers on monitoring, alerts, uptime, SLO, SLA, error rate, on-call, pager, alert fatigue, observability, dashboards, what should we monitor. Also triggers when an incident reveals a gap in monitoring.
Design and review logs, metrics, traces, SLOs, and alerting for reliable systems. Use for telemetry strategy and coverage gaps. NOT for live incident command or vendor-specific setup.
Any component rendered many times — cards, list rows, table cells, nav items, tiles, KPI widgets, feed entries — is a fixed slot model, not a free-form box. The same slots appear in the same place in every instance and stay aligned across siblings even when text and values vary in length. Reserve space for optional slots, pin anchor elements (CTA, price, value), clamp overflowing text, and give the full value back via title/tooltip. Use when building or reviewing any repeated component whose content length differs between instances.
Desenvolve e testa slogans coerentes com a estratégia e a voz. Use para criar uma assinatura verbal curta ou revisar uma frase existente.
Feishu Calendar (calendar): Provides comprehensive management capabilities for calendars and schedules (meetings). Core scenarios include: viewing/searching schedules, creating/updating schedules, managing attendees, checking free/busy status, and recommending available time slots. For high-frequency operations, prioritize using Shortcuts: +agenda (quick overview of today's/upcoming schedules), +create (create a schedule and invite attendees as needed), +freebusy (check the free/busy status of the user's primary calendar and RSVP status), +suggestion (provide multiple time recommendation solutions for appointment schedule requests with undetermined times).
Audit and optimize Convex application performance, covering hot path reads, write contention, subscription cost, and function limits. Use when a Convex feature is slow, reads too much data, writes too often, has OCC conflicts, or needs performance investigation.
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.
Use this skill when encountering errors, bugs, performance issues, or unexpected behavior in an InsForge project — from frontend SDK errors to backend infrastructure problems. Trigger on: SDK returning error objects, HTTP 4xx/5xx responses, edge function failures or timeouts, slow database queries, authentication/authorization failures, realtime channel issues, backend performance degradation (high CPU/memory/slow responses), edge function deploy failures, or frontend Vercel deploy failures. This skill guides diagnostic command execution to locate problems; it does not provide fix suggestions.
Use for anything related to EAS Observe — adding `expo-observe` to an Expo project (AppMetricsRoot/ObserveRoot HOC, markInteractive, the useObserve hook, and the Expo Router / React Navigation integrations for per-route metrics), querying via the EAS CLI (`eas observe:metrics-summary`, `observe:metrics`, `observe:routes`, `observe:events`, `observe:versions`), or interpreting the resulting metrics (cold/warm launch, TTR, TTI, navigation cold/warm TTR, update download, and the TTI frameRate params for triaging slow startups).
Inspect and profile React Native component trees from agent-device. Use when debugging React Native props, state, hooks, render causes, slow components, excessive re-renders, or questions like why a component re-rendered.
Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.