'reliability' 검색 결과

Resilience testing specialist for failure injection, game day planning, and building confidence in system reliabilityUse when "chaos engineering, resilience testing, failure injection, game day, fault tolerance, chaos experiment, disaster recovery, reliability testing, chaos-engineering, resilience, failure-injection, game-day, fault-tolerance, reliability, testing, litmus, chaos-monkey, ml-memory" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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 benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates guidance for reliability, resilience, availability, redundancy, fault-tolerance, and disaster recovery (DR) for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use when the user asks to evaluate, design, or improve the reliability, resilience, availability, or disaster recovery capabilities of Google Cloud workloads.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates reliability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use this skill to evaluate a workload, identify reliability requirements, and provide actionable recommendations for build, deploy, and manage the workload reliably in Google Cloud.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when working with Blameless — blameless incident management, SLO tracking, retrospectives, and reliability insights. Covers incident lifecycle, blameless retrospective facilitation, follow-up tracking, reliability scorecards, and incident type categorization. Use when managing active incidents, conducting retrospectives, tracking SLO compliance, or analyzing reliability trends in Blameless.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Google SRE (Site Reliability Engineering) practices distilled from the SRE book series and real Google Brain/Meta/ByteDance production experience. Covers SLI/SLO/Error Budget design, incident response, capacity planning, toil automation, monitoring golden signals, and chaos engineering principles. USE WHEN: designing reliability targets, setting up monitoring/alerting, planning capacity, reducing operational load, building incident response runbooks, implementing chaos engineering, defining error budgets, or establishing reliability culture that balances velocity with stability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implements reliability patterns including circuit breakers, retries, fallbacks, bulkheads, and SLO definitions. Provides failure mode analysis and incident response plans. Use for "SRE", "reliability", "resilience", or "failure handling".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete observability & reliability engineering system. Use when designing monitoring, implementing structured logging, setting up distributed tracing, building alerting systems, creating SLO/SLI frameworks, running incident response, conducting post-mortems, or auditing system reliability. Covers all three pillars (logs/metrics/traces), alert design, dashboard architecture, on-call operations, chaos engineering, and cost optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

코드와 에이전트 결과물을 검증하고 신뢰도를 점수로 확인합니다. 기준 미달 변경의 자동 되돌리기, 품질 추이 모니터링과 보고서 출력도 다룹니다.

• AI가 생성한 코드 검증하기
• 검증에 실패한 변경 되돌리기
• 시간에 따른 품질 변화 확인하기

Find candlestick and chart patterns in previously ingested market data. Classify detections by reversal or continuation, bullish or bearish direction, and reliability score, then rank and store them for later reference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding candlestick patterns for a symbol and period
• Reviewing patterns ranked by reliability
• Organizing bullish and bearish signals

Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow agent responses
• Track token usage and estimated costs
• Trace failures in model and tool calls

Review an AI workflow across five areas: prompts, use of context, tools, system structure, and safety. Receive scores, specific findings, and prioritized actions to address quality and reliability problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check an AI workflow before production use
• Audit an existing agent for quality and reliability
• Prioritize fixes for workflow problems

Design or review distributed systems, where multiple computers work together, using 42 cloud design patterns. Organizes options for reliability, performance, messaging, security, and deployment, emphasizing why to choose each pattern and the trade-offs involved.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a distributed system architecture
• Choose patterns for failures and recovery
• Compare caching, scaling, and load management approaches

Examine AI product reliability, learning methods, and model limitations through frameworks drawn from Andrej Karpathy’s public statements. Assess the gap between demonstrations and real use, uneven capabilities, and where people should retain control.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing AI product reliability beyond a demo
• Planning hands-on study of neural networks
• Examining LLM strengths and failure patterns

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Plan a system or service from requirements through components, APIs, data storage, and reliability. Produce a structured design document with diagrams, stated assumptions, trade-offs between choices, and decisions to revisit as usage grows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning the architecture of a new service
• Defining APIs, data models, and service boundaries
• Reviewing scaling and reliability plans

Build three-tier bookings forecasts from sales opportunities and historical conversion data. Project recurring revenue by customer cohort, assess the reliability of each sales stage, and present forecast numbers alongside their assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a quarterly bookings forecast for the board
• Projecting annual recurring revenue for the next 4–8 quarters
• Finding customer cohorts with declining revenue retention

Review architecture and engineering growth plans through six questions covering capacity limits, technical debt, staffing, build-versus-buy costs, reliability, and security. Produce a structured verdict and three next steps before committing to major technical decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a major architecture change
• Checking readiness for tenfold traffic growth
• Comparing in-house development with a vendor

Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure alerts for agent latency and error rates
• Monitor rapid increases in token usage
• Monitor safety triggers and permission denials

Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing two prompts on the same test cases
• Checking quality after agent configuration changes
• Turning real failures into repeatable evaluation cases