「slos」の検索結果

Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.

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。

Reliability engineering for cloud systems — SLIs, SLOs, error budgets, SRE practices, runbooks, incident response, on-call rotation, blameless postmortems, chaos engineering, and the NASA Systems Engineering methodology (MCR/SRR/PDR/CDR/ORR phase gates, TAID verification, requirements tracing) adapted to cloud operations. Use when establishing SLOs for a new service, running an incident, writing a runbook, preparing a launch readiness review, or bringing NASA SE discipline to cloud deployments.

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 en Datadog (APM, logs, infrastructure, monitors, dashboards, SLOs)

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 implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

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 comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

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。

Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.

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。

[omh] Postmortem for an outage or SLO miss: postmortems, SLOs, error budgets, incident follow-ups, and service reliability evidence. Use when the user says: reliability-review, reliability review, incident review, incident postmortem, postmortem, post-mortem, slo review, slo.

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。

Tune Alchemy-backed reads with measured latency, cache semantics, batching, concurrency, and freshness SLOs. Use when an integration is slow or wasteful. Trigger with "optimize Alchemy performance", "cache Alchemy data", or "reduce Alchemy latency".

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 implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

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 comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

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 or troubleshoot telemetry, SLOs, and incident diagnostics. Route to the relevant signal, system boundary, or vendor guide.

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 and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.

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 check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".

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 and run a monitoring system for a website or web app. Use this skill when setting up uptime checks, defining SLOs, configuring error tracking, choosing what to alert on, designing on-call rotations, or fixing alert fatigue. Triggers on monitoring, alerts, uptime, SLO, SLA, error rate, on-call, pager, alert fatigue, observability, dashboards, what should we monitor. Also triggers when an incident reveals a gap in 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。

Design observability strategies: SLI/SLO frameworks, alerting, and dashboards. Use when instrumenting a production service, tuning alert rules, designing Grafana dashboards, defining SLOs and error budgets, or reducing alert fatigue.

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。

Infrastructure as Code patterns (Terraform, Kubernetes), observability design (SLOs, metrics, alerting, dashboards), and pipeline security stages. Load when designing infrastructure, observability, or security scanning.

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。

When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.

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 and SRE expert. Use when setting up monitoring, logging, tracing, defining SLOs, or managing incidents. Covers Prometheus, Grafana, OpenTelemetry, and incident response best practices.

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 or audit SLOs, SLIs, alert rules, incident response steps, escalation paths, postmortems, operational runbooks, and customer-impact communication. Use when defining production reliability, preparing launch readiness, responding to an outage, writing a runbook, tuning alerts, or closing the loop after an incident.

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 monitoring strategy, metrics collection, and alerting thresholds during PRD v0.8 Deployment & Ops. Triggers on requests to set up monitoring, define alerts, or when user asks "what should we monitor?", "alerting strategy", "observability", "metrics", "SLOs", "dashboards", "monitoring setup". Outputs MON- entries with monitoring rules and alert configurations.

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 or review SLOs and SLAs for a service. Use when a senior developer needs reliability targets.

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。

数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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 error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error handling, logging, monitoring, observability, OpenTelemetry, error boundaries, circuit breakers, retry logic, or SLO tracking. Do NOT use for incident runbooks (use runbook-gen), vendor-specific APM setup (Datadog, Sentry agent config), or K8s 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。

Provides Site Reliability Engineering best practices for SLOs, SLIs, SLAs, error budgets, toil reduction, reliability reviews, and capacity planning. Use when defining service objectives, measuring reliability, reducing toil, planning capacity, or when user mentions 'SRE', 'SLO', 'SLI', 'SLA', 'error budget', 'toil', 'reliability', 'on-call', 'capacity planning'.

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。