2,523 件 ・ 関連度順
概要と使いどころ
Use when collecting or analyzing production iOS or iPadOS performance telemetry with MetricKit, including iOS 27 MetricManager async metric or diagnostic reports, hang or crash triage, custom signposts, extended launch measurement, durable export, or iOS 26 MXMetricManager compatibility.
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 "verify UI changes", "test every screen and action", "run regression QA", "check a deployed UI", or "validate an extension release". Test known web and browser-extension workflows on localhost, staging, or production with action coverage, recovery, localization, accessibility, and real integration evidence. Use dogfood for open-ended bug discovery.
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 "write a test", "review these tests", "audit tests", "prune low-value tests", or "find duplicate tests", and whenever writing, changing, reviewing, or sweeping tests. Gates new tests and audits low-value, implementation-coupled, or duplicative tests and the test-only production seams they demand.
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。
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
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 user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
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 and operates a LiveKit agent in production: shipping a version to LiveKit Cloud and rolling it back, secrets and configuration, the worker process model and prewarming, safe async inside worker processes, provider timeouts and degradation, graceful shutdown, SDK upgrades, and observability. Use when the user says "deploy my agent", "roll back the deployment", "tail the agent logs", "the first call after a restart is slow", "attached to a different loop / event loop is closed", "prewarm the VAD", "shut down without dropping calls", "upgrade livekit-agents safely", "correlate logs by session", or is changing an agent codebase that is already live. Not for designing the agent (building-livekit-agents) or reproducing one bad conversation locally (debugging-livekit-agents).
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。
Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.
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。
Deploy frontend and full-stack apps on Vercel with previews, edge functions, environment promotion, and production guardrails. Use when shipping Next.js, SvelteKit, or static sites with zero-config CI/CD.
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。
Deploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.
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。
Harden OpenClaw self-hosted environments with baseline host controls, auth tightening, secret handling, network segmentation, and safe update/rollback workflows. Use when deploying OpenClaw in home labs, startups, or production-like local AI infrastructure.
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 and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment 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。
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
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。
Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.
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。
Apply CIS benchmarks and secure Linux servers. Configure SSH, manage users, implement firewall rules, and enable security features. Use when hardening Linux systems for production or meeting security compliance 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。
Operate MySQL-compatible databases on PlanetScale with branching workflows, safe migrations, and production rollouts.
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。
Configure zero-downtime deployment strategies including blue-green, canary, and rolling deployments. Implement traffic shifting, health checks, and rollback procedures. Use when implementing production deployment strategies or zero-downtime releases.
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。
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
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。
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
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。
Deploy, scale, and manage Kubernetes workloads. Create deployments, services, and configurations, manage cluster resources, troubleshoot pods, and implement production-ready Kubernetes patterns. Use when working with Kubernetes clusters, K8s deployments, or container orchestration.
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。
Implement incident management processes and escalation procedures. Configure on-call schedules and post-incident reviews. Use when managing production 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。
Build production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance across cloud and self-hosted inference.
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。
Set up alerting rules, configure on-call rotations, and manage incident response workflows. Integrate with PagerDuty, Opsgenie, or Grafana OnCall for alert routing and escalation. Use when implementing alerting strategies and on-call management for production systems.
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。
Implement change management processes. Configure CAB reviews, change windows, and rollback procedures. Use when managing production changes.
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。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
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。