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概要と使いどころ
Email delivery infrastructure - DNS authentication (SPF/DKIM/DMARC), subdomain isolation, provider abstraction, template systems, bounce handling, warmup strategy, and deliverability 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。
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
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。
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
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。
Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.
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。
Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
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 SharpConsoleUI to build full terminal (TUI) applications in .NET — equally suited to full-screen single-window apps and multi-window desktops with overlapping draggable windows — using a compositor, a DOM layout engine, and 40+ reactive controls (data tables, tree views, forms, an embedded PTY terminal, markdown, charts, video). USE FOR: interactive console apps, full-screen TUIs, multi-window terminal desktops, dashboards, wizards, and admin/monitoring UIs that need focus, mouse, and flicker-free rendering over local terminals or SSH; NativeAOT console tools. DO NOT USE FOR: simple line-based CLI output or argument parsing; non-interactive scripts; GUI/desktop (WPF/WinForms) or web UIs. INVOKES: inspect the project, add the SharpConsoleUI package, scaffold a window/control tree, and build/run to verify the app renders.
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。
System hardening, detection engineering, security baseline monitoring, patch management, defense-in-depth architecture, and security posture improvement
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。
Volcengine Cloud Detect (云拨测) API integration for creating detection tasks, retrieving results, generating reports, and managing detection nodes. Use when the user wants to (1) create network detection/dialing tasks (拨测任务), (2) check task results or generate detection reports, (3) list or search detection nodes (拨测节点), (4) manage existing tasks (list/stop/restart/delete). Triggers include natural language requests about website monitoring, network latency testing, HTTP detection, ping tests, DNS tests, node queries, or report generation for Volcengine cloud detect service.
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 reviewing, designing, or modifying Java enterprise systems that may support financial entities, critical ICT services, third-party ICT provider integrations, or operational resilience obligations under DORA. This should trigger for requests such as Review a Java platform for DORA ICT risk controls; Design operational resilience evidence for a financial service; Add incident, continuity, backup, recovery, or third-party ICT controls; Assess resilience testing and monitoring before production release. Part of Plinth Toolkit
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 reviewing, designing, or modifying Java enterprise systems that may support EU Market Abuse Regulation concerns, market surveillance, suspicious order and transaction reports, insider dealing controls, unlawful disclosure controls, market manipulation detection, inside information disclosure workflows, insider-list evidence, PDMR transaction notifications, alert explainability, model or rule provenance, reviewer decisions, or compliance escalation. This should trigger for requests such as Review a Java trading surveillance system for MAR controls; Design suspicious order and transaction monitoring evidence; Add alert explainability and reviewer-decision audit trails; Assess AI-assisted market-abuse detection before production release. Part of Plinth Toolkit
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 you need to implement or improve Java logging and observability — including selecting SLF4J with Logback/Log4j2, applying proper log levels (ERROR, WARN, INFO, DEBUG, TRACE), parameterized logging, correlation context, secure logging without sensitive data exposure, environment-specific configuration, log aggregation, monitoring, and alerting. This should trigger for requests such as Improve logging; Apply logging; Refactor logging; Add logging support; Review SLF4J structured logging in Java code. Part of Plinth Toolkit
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 an eval plan for an AI feature - error analysis on real traces, failure-mode ranking, code checks and binary LLM judges validated against human labels, CI tests, production monitoring, and a launch bar. Use when you ask how do we eval this, how do I test an AI feature, our LLM outputs are inconsistent, are the evals good enough, LLM-as-judge, hallucinations, or what accuracy is good enough to launch. Draws on 41 Lenny's Podcast guests incl. Hamel Husain, Shreya Shankar and Sander Schulhoff.
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。
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
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。
Social media monitoring, narrative tracking, and OSINT. Use when tracking viral spread, coordinated campaigns, or account vetting.
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。
Web page change detection, availability tracking, and RSS feed generation. Use to monitor changes, downtime, or make a feed.
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。
Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).
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。
Wash sale detection under 2025 US crypto rules with 61-day window monitoring, disallowed loss tracking, and safe re-entry countdown
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。
Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens
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。
Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading
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。
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
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。
Add Sentry v8 error tracking and performance monitoring to your project services. Use this skill when adding error handling, creating new controllers, instrumenting cron jobs, or tracking database performance. ALL ERRORS MUST BE CAPTURED TO SENTRY - no exceptions.
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, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript 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。
Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.
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。