23 件 ・ 関連度順
概要と使いどころ
Use when working with Betteruptime — better Uptime monitoring, status pages, incident management, on-call scheduling, and heartbeat tracking. Covers monitor configuration, response time analysis, incident lifecycle, on-call rotations, and SLA reporting. Use when managing uptime monitors, reviewing incident history, configuring on-call schedules, or analyzing response times in Better Uptime.
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。
Better Stack (formerly Better Uptime + Logtail) is an observability platform that combines uptime monitoring, log management, incident response with on-call escalation, and status pages. Use when a user asks to set up uptime monitors or heartbeat monitoring for cron jobs, create escalation policies, send logs with @logtail/node or the Pino transport, manage Better Stack through its API or Terraform provider, or publish status page updates.
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 this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation
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。
Monitor website uptime - check availability, response times, and status
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 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.
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。
Audit a project's infrastructure and SaaS stack, then produce a cost report showing what the user currently pays and what they would save by consolidating onto Temps (self-hosted or Temps Cloud). Detects hosting platforms (Vercel, Netlify, Railway, Render, Heroku, Fly.io), analytics (PostHog, Plausible, Mixpanel, Amplitude, Fathom), error tracking (Sentry, Bugsnag, Rollbar, Honeybadger), session replay (LogRocket, FullStory, Hotjar, Highlight), uptime monitoring (Pingdom, UptimeRobot, Better Stack, Checkly), managed databases (Supabase, Neon, PlanetScale, MongoDB Atlas, Upstash, RDS), and transactional email (SendGrid, Postmark, Resend, Mailgun) from dependencies, config files, and env var names. Use when the user wants to: (1) Know how much they would save by switching to Temps, (2) Audit their SaaS/infrastructure spend, (3) Compare their current stack's cost against self-hosting, (4) Decide whether Temps is worth it, (5) Build a business case for consolidating tools. Triggers: "how much would I save", "temps savings", "cost comparison", "audit my stack", "am I overpaying", "saas spend", "calculate savings", "is temps cheaper".
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 working with Better Stack — better Stack (formerly Logtail) for log search, uptime monitoring, incident management, on-call scheduling, and status page management. Covers log querying, monitor configuration, heartbeat checks, escalation policies, and team management. Use when searching logs, managing uptime monitors, handling incidents, or configuring on-call schedules via Better Stack API.
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 working with Atlassian Statuspage — atlassian Statuspage management, component monitoring, incident communication, scheduled maintenance, and subscriber notifications. Covers page configuration, component group management, incident lifecycle, metric tracking, and uptime reporting. Use when managing public or private status pages, communicating outages, scheduling maintenance, or reviewing component uptime in Atlassian Statuspage.
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。
Product-fleet ops desk (Busabase App-in-Skill) for uptime/service health, SSL certificate expiry, domain renewal, API key rotation, cloud spend anomalies, and a review queue of agent-proposed action cards. Use when the user invokes $kelly-devops or /kelly-devops, or asks about uptime, service health checks, SSL certificate expiry, domain renewal, domain expiry, API key rotation, token rotation reminders, cloud spend anomaly, billing spikes, ops desk, incident review, or reviewing agent-proposed action cards (renew domain, rotate key, investigate spend, restart service, acknowledge incident).
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。
Reference: review of SaaS subscription agreements with attention to the terms that matter most in subscription deals — auto-renewal mechanics, price escalation, data portability, uptime SLAs, and subprocessor rights. Loaded by /commercial-legal:review when a SaaS or subscription agreement is detected.
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。
Tech debt assessment that identifies, classifies, and prioritizes maintainability problems — helping teams build a refactoring roadmap — drawing on twelve classic engineering books. Triggers when: user asks about tech debt, refactoring priorities, what to clean up first, or asks "why is this so hard to change?", "what should we fix first?", or "how do I justify refactoring to management?". Do NOT trigger for: server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime — "health" in those contexts is infrastructure, not code quality. Also not for single-function refactoring questions.
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。
Combined codebase health dashboard that scores a project across all four quality dimensions — PR quality, architecture, tech debt, and test quality — in a single pass, drawing on twelve classic engineering books. Triggers when: user wants an overall quality assessment, asks "how healthy is this codebase?", "run all the checks", "I need a health score before the release", or wants to onboard a new team with a quality overview. Do NOT trigger for: server health checks, HTTP health endpoints, Kubernetes liveness/readiness probes, database health, or application uptime. Also do not trigger when the user specifically requests only one dimension — use the corresponding focused skill instead (brooks-review / brooks-audit / brooks-debt / brooks-test).
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。
Full-sweep mode: runs a unified analysis across all quality dimensions — code decay, architecture, tech debt, and test quality — then applies fixes directly to the codebase. Safe changes are auto-applied; risky changes are confirmed before execution. Drawing on twelve classic engineering books. Triggers when: user wants to "fix everything", "sweep the codebase", "auto-fix all issues", "clean up the whole project", or asks for a single command that both diagnoses and remediates quality problems. Do NOT trigger for: read-only audits or health reports where the user only wants findings without code changes; single-dimension reviews (use the focused skill instead: brooks-review / brooks-audit / brooks-debt / brooks-test); server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime.
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。
Scheduled probes that run CONTINUOUSLY after release. Covers probe design for critical user journeys, alerting integration, SLA validation, multi-region monitoring, and the boundary between QA and SRE. Use when: "synthetic monitoring," "uptime testing," "scheduled probes," "SLA validation," "availability monitoring," "post-deploy checks." Not for: safe-release techniques during rollout — use testing-in-production. Not for: designing tests from prod telemetry — use observability-driven-testing. Not for: a one-shot post-deploy smoke gate tied to a release — use release-readiness. Related: testing-in-production, release-readiness, performance-testing, qa-metrics.
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。
Queries all configured services for health status — uptime, restarts, errors, resources. Use when checking the health of running services.
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。
Computes availability for redundant and series designs, failover, SPOFs, fault trees, FMEA, Weibull, and error budgets. Use when asking what uptime a design gives.
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。
Uses AWS CloudWatch SDK (boto3) to configure anomaly detection bands on metrics via PutAnomalyDetector API. Integrates with SNS for notifications and CloudWatch Synthetics for canary-based uptime 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。
Establish Service Level Objectives (SLO), Service Level Indicators (SLI), and Service Level Agreements (SLA) with error budget tracking, burn rate alerts, and automated reporting using Prometheus and tools like Sloth or Pyrra. Use when defining reliability targets for customer-facing services, balancing feature velocity against system reliability through error budgets, migrating from arbitrary uptime goals to data-driven metrics, or implementing Site Reliability Engineering practices.
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 a task needs the judgment of an Embedded/Firmware Engineer — analyzing worst-case execution time for a hard real-time deadline, choosing static over dynamic memory allocation to avoid fragmentation on a long-uptime device, keeping an interrupt service routine minimal and deferring work to the main loop, synchronizing a variable shared between an ISR and main context to prevent a torn read, or verifying a hardware register interaction against the datasheet's exact timing rather than observed bench behavior.
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 a task needs the judgment of a Computer and Information Systems Manager (IT Manager/Director/CIO-adjacent) — planning IT infrastructure and systems strategy, evaluating a technology vendor or platform decision, allocating an IT budget, or managing IT risk (security, uptime, technical debt) at an organizational level. Broader than the devops-sre or software-engineer roles — this one owns IT as a business function, not a specific engineering discipline.
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 a task needs the judgment of a Chief Technology Officer — deciding build vs buy for a core system, choosing whether an architecture bet is worth a full rewrite, sizing an SLA/uptime commitment against engineering cost, structuring engineering teams around a product roadmap, or writing the technical risk section of a board update.
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。
Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.
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 working with Cachet — cachet open-source status page management, component tracking, incident reporting, and metric visualization. Covers component group management, incident lifecycle, scheduled maintenance, metric points, and subscriber management. Use when managing self-hosted status pages, creating incidents, tracking component health, or reviewing uptime metrics in Cachet.
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。