'observability' 검색 결과

Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing production readiness
• Planning monitoring and logging
• Improving incident response procedures

Assess whether a GKE cluster or application is ready for production. Coordinates specialist skills to review scaling, security, reliability, monitoring, recovery, costs, and maintenance, then reports domain status, a weighted readiness score, and remediation priorities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a GKE application before its production launch
• Assess production readiness across an entire cluster
• Prioritize fixes using domain status and a readiness score

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Configure Cloud Logging within one Google Cloud project. Set storage locations, retention periods, routing rules, and viewing permissions; create metrics from logs; and adjust stored volume through exclusions or sampling, which retains only a portion of entries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set regional log storage and retention periods
• Restrict access to sensitive logs
• Count specific errors with logs-based metrics

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Record and query iii engine logs, traces that show how calls flow, and operational metrics. Set up reactions to incoming errors, inspect alerts, and review metrics aggregated over time windows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating errors through related logs and traces
• Triggering notifications when error logs arrive
• Reviewing metrics aggregated over time

Design service monitoring with Prometheus, write PromQL queries to analyze metrics, and build Grafana dashboards. Configure actionable alerts and manage metric labels so monitoring stays useful as the volume of collected data grows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitor service latency, traffic, and errors
• Review PromQL queries and precomputed metrics
• Group related alerts and filter brief spikes

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Audit server-side advertising measurement by comparing browser and server events, checking duplicate handling, consent, and data quality. Reconcile platform diagnostics with source logs and business conversions, then return prioritized findings, owners, and verification steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate missing or duplicated conversion events
• Compare browser and server event data
• Review consent and data handling before transmission

Assess an existing Convex app and turn the findings into a prioritized improvement plan. With explicit approval, update outdated components, address production error monitoring gaps, and reassess the app to show how its score changed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an existing Convex app before release
• Updating outdated Convex components
• Adding production error monitoring

Diagnose why an AI agent skips tools, passes incorrect arguments, or repeats calls. Get guidance on tool descriptions, schemas that define accepted inputs, execution flow, and mock tests before connecting to a real model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding why an agent never calls a tool
• Fixing incorrect or missing tool arguments
• Investigating repeated calls or skipped steps

Write and review Cloudflare Workers code using current official guidance. Check configuration, asynchronous tasks, large response handling, secrets, and service connections, with findings supported by documentation, code locations, or validation results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing a new Cloudflare Worker
• Reviewing Workers code before production use
• Checking wrangler.jsonc and service bindings

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings

Instrument Python or TypeScript agents to record runs for Failproof AI, verify local event files, and troubleshoot missing sessions. The skill also guides building evaluator workers that score completed runs on your own infrastructure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning where an agent should record events
• Adding run logging to an existing agent
• Troubleshooting missing events or sessions

Add OpenTelemetry tracing to frontend and backend flows. Connect execution records across services and background jobs, record phase durations and entity IDs, and mark failures so developers can investigate slow or broken operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracing a user action across multiple services
• Investigating slow queues and background jobs
• Connecting missing or disconnected traces

Automatically trace Claude Code conversations to Braintrust for observability. Captures sessions, conversation turns, and tool calls as hierarchical traces.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when migrating applications, examples, integrations, documentation, manifests, or repository code from NeMo Flow to NeMo Relay across Python, Rust, Node.js, Go, C FFI, CLI, configuration, and observability surfaces.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when the user is deploying or operating the DOCA Argus Service — the packaged BlueField-side runtime-security container that watches the BlueField and attached host for suspicious activity, integrity violations, and operational anomalies, and forwards findings to a SIEM (Splunk / ELK / Sentinel / syslog). Covers the four-axis config (detection policy, forwarding, sampling, host coverage), running the NGC container on BlueField Arm, and wiring the forwarder. Trigger even without "DOCA Argus" by name — typical implicit phrasings: "container green but no findings arrive", "false-positive flood in Splunk", or "runtime security on a fleet of BlueField-3s". Refuse and route elsewhere for installing DOCA, SIEM-side ingest stanzas, pre-baked detection-rule packs, and metrics observability (DOCA Telemetry). Argus is NVIDIA's currently- promoted runtime-security framework, superseding the older App Shield library; name it first for new runtime-security work.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[omh] Imminent production launch or release: evaluate release, deploy, security, observability, rollback, docs, and support readiness without claiming production access. Use when the user says: production-audit, production audit, production readiness, prod audit, prod readiness, ready for production, ready to ship, ship readiness.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability. Triggers: "Weights and Biases", "W&B", "WeightsAndBiases", "ML experiment tracking", "model registry", "experiment management", "wandb".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.