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概要と使いどころ
Generate domain permutations with dnstwist and check DNS resolution to detect typosquatting, homograph phishing, and brand impersonation domains registered against your organization. Use when asked to monitor for lookalike domains, investigate a phishing domain, or assess brand-impersonation risk.
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。
Safely monitor ransomware group Tor-hosted data leak sites (DLS) to collect and extract structured victim posting data, track group activity trends over time, and produce sector- and geography-specific ransomware risk assessments. Use when performing threat intelligence gathering on active ransomware groups or building proactive defense reporting from double-extortion leak-site activity.
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 Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
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。
Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy 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。
Use when the user wants to integrate with the X (Twitter) API via Xquik to search tweets, look up user profiles, extract followers, run giveaway draws, monitor accounts, or access trending topics. Also use when the user mentions 'Xquik,' 'Twitter API,' 'X API,' 'tweet scraper,' 'follower extraction,' or 'Twitter monitoring.' Covers REST API, webhooks, and MCP server setup.
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。
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
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。
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and 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。
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
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 a monitoring dashboard to NanoClaw. Installs @nanoco/nanoclaw-dashboard and a pusher that sends periodic JSON snapshots.
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。
Brain-augmented web research. Sends brain context about a topic to Perplexity, which searches the web with citations and returns what is NEW vs what the brain already knows. Use for entity enrichment, current-state checks, deal monitoring, and freshness deltas. NOT for simple URL fetches (use web_fetch) or brain-only queries (use gbrain query).
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。
FastGPT Docker Compose self-hosting deployment workflow. Use when a user asks an AI agent to deploy FastGPT with Docker by referencing /deploy/SKILL.md, including creating an empty fastgpt directory, running the install script in non-interactive mode, starting Docker Compose, monitoring service health, troubleshooting compose issues, and returning the access URL plus root credentials and next steps.
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。
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
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 the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private/age-restricted videos.
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 production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.
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。
Land a PR by monitoring conflicts, resolving them, waiting for checks, and squash-merging when green; use when asked to land, merge, or shepherd a PR to completion.
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。
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
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。
Diagnose GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` logs and hang monitored events) using the ML Diagnostics `Megascale XLA (MXLA) Hang Analyzer` (`gcloud alpha mldiagnostics monitored-events`) and 1-minute Cloud Monitoring multi-slice latency metrics (`kubernetes.io/container/multislice/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction restarts (use gke-ai-troubleshooting-jobset-interruption).
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。
Deploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-onboarding or product-specific skills instead).
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。
Configures Cloud Monitoring PromQL-based Service Level Objective (SLO) alerting policies on Google Cloud for resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting 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。
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
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。
Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident 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。
Inspects Filestore capacity and utilization on Google Cloud, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.
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。