「monitor」の検索結果

Generate unit, integration, E2E, and visual regression tests following the Testing Trophy methodology (80% integration). Covers Vitest/Jest, Testing Library, Playwright, MSW for API mocking, snapshot strategy, visual regression (Chromatic/Percy/Playwright), test factories with Faker, and CI sharding. Use when user asks to write tests, set up testing framework, mock API/dependencies, improve coverage, or add visual regression. Do NOT use for performance/load testing, production monitoring, or type testing (covered by TypeScript).

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。

Windows 11 system administration with PowerShell — system info reporting, hardware monitoring, package management (winget/scoop), disk cleanup, performance optimization, environment configuration, PowerShell profile setup with aliases and autocomplete, and Task Scheduler automation. Use when user asks to check system info, install tools on Windows, clean up disk space, set up PowerShell profile, or automate Windows tasks. Do NOT use for Linux administration, cross-platform scripting, or network infrastructure management.

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。

Automatically notify the user when long-running terminal commands finish (npm test, docker build, git push, etc.). The agent monitors command execution and sends a desktop notification on completion if the command ran longer than threshold (default 10s). Use when user asks to "notify me when done", "desktop notification when command finishes", "alert when done", or "tell me when this completes". Do NOT use for interactive commands (vim, nano, less, htop), commands that always complete in <5s, or streaming commands (tail -f).

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。

Browser automation, web scraping, E2E testing, and visual regression with Playwright. Covers 30+ patterns: login flows, form testing, responsive design checks, broken link validation, API mocking, data extraction, PDF generation, accessibility audits (axe-core), performance budgets (Lighthouse), visual diffing, multi-browser testing (Chromium/Firefox/WebKit), mobile emulation, infinite scroll, shadow DOM, iframes, file downloads, auth state reuse, cookie consent handling, WebSocket monitoring, console error detection, HAR export, trace viewer, Docker CI, GitHub Actions, and parallel sharding. Use when user asks to test a website, take screenshots, check responsive design, automate a browser task, scrape data, validate forms, check broken links, test login, audit accessibility, or measure page performance. Do NOT use for unit testing, API-only testing, or static analysis. Requires Node.js 18+.

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。

Design error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error handling, logging, monitoring, observability, OpenTelemetry, error boundaries, circuit breakers, retry logic, or SLO tracking. Do NOT use for incident runbooks (use runbook-gen), vendor-specific APM setup (Datadog, Sentry agent config), or K8s debugging.

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 acting as a project manager that delegates tasks to other coding-agent sessions (Claude Code, Codex, or Pi) - launch workers, assign them work, monitor progress, review their tool calls, and collect results

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。

This skill should be used when the user asks to "use bilibili API", "download bilibili video", "get bilibili user info", "list bilibili favorites", "send bilibili danmaku", "upload video to bilibili", "monitor bilibili live room", "search bilibili", "get bilibili comments", or needs guidance on the bilibili_api Python library usage, authentication, API endpoints, or workflow patterns.

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。

Home Assistant YAML configuration and automation. The most common skill for general HA questions. Covers automations, blueprints, scripts, scenes, template sensors, Lovelace dashboards, Mushroom cards, packages, helpers, presence detection, voice Assist, calendar automation, Jinja2 templates, notification patterns, energy monitoring, Matter devices, automation debugging and traces, dashboard sections view, and trigger-based template entities.

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。

Aurora Smart Home orchestrator — routing layer for all smart home skills. Use this skill when the user asks ANY smart home question and you need to decide which skill to invoke, or when a task spans multiple skills (e.g., "build a sensor that shows on a dashboard and triggers automations"). Invoke aurora FIRST before reaching for a specific skill — it will route to the right specialist(s) and recommend the correct Claude model to keep token usage efficient. Trigger on: smart home, Home Assistant, ESPHome, automation, IoT, dashboard, ESP32, Node-RED, or any request about controlling or monitoring devices at home.

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。

Bug-fix routine that fixes the whole class of bug, not just the reported instance. Use for any bug report, failing production behaviour, monitoring alert, audit finding or review finding that does real harm (a smaller review finding goes on the item's list, as ship-check says), and whenever the same kind of bug has been seen before. Reproduces it with a failing test, names the failure class, searches the task's scope for the same pattern (the whole codebase when the user's own prompt asks for the fix, not when it is picked from a task's list), fixes or records every hit, and adds the rule, invariant, helper or check that stops it coming back.

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。

Subscribe to live Hyperliquid data over WebSocket from the desk computer - mids, order book, trades, candles, best bid/offer, and per-account fills, order updates and events - with raw JSON, Python SDK and TypeScript examples, plus how to run a supervised watch that logs to a file and alerts. Read-only. Use for monitoring, fill notifications and any watch that polling would make expensive.

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。

How the desk watches markets and the account between trades using Grok Bot routines and WebSocket or polling watches - the desk brief, book checks, funding and price watches, alert conditions and what a watch may and may not do. Use when the user asks for briefings, alerts, "watch X" or scheduled checks.

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。

Builds production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.

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。

Runs production ML and LLM systems. Use when deploying, canarying, rolling back, monitoring drift or feature skew, promoting champions, migrating providers, or handling incidents.

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。

Help order food timed to arrive at a specific time. Works backward from target arrival, suggests restaurants, builds cart, and monitors delivery.

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。

Shift-left safety net for dbt/SQL model edits. Runs change impact assessment before edits, generates SQL validation queries after, and executes them via `/mc-validate run`. Delegates health and monitor creation to peer skills.

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, edit, validate, and import Monitors-as-Code YAML files. CLI-first; falls back to MC MCP tools, then manual validation.

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 and manage Grafana dashboards and Prometheus metrics for Happy infrastructure. Covers grafanactl CLI usage, direct Prometheus queries through Grafana proxy, and dashboard-as-code workflows. Use when user asks about metrics, dashboards, monitoring, Grafana, Prometheus, or wants to add/modify panels.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

Automating browsers via Playwright and Chrome DevTools for data collection, form interaction, screenshot capture, and network monitoring. Task completion focus (vs Voyager for E2E testing).

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。

Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP analysis.

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。

Post-deploy canary monitoring. (gstack)

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。

Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.

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。

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

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。