observability▌

You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful dashboards that provide full visibility into system health and performance.

Configure Fluentd and Fluent Bit for centralized log aggregation, routing, filtering, and enrichment across distributed infrastructure

Implements eBPF-based security monitoring using Cilium Tetragon for real-time process execution tracking, network connection observability, file access auditing, and runtime enforcement. Covers TracingPolicy CRD authoring with kprobe/tracepoint hooks, in-kernel filtering via matchArgs/matchBinaries selectors, JSON event export, and integration with SIEM pipelines. Use when building kernel-level runtime security observability for Linux hosts or Kubernetes clusters.

Implement eBPF-based runtime security observability and enforcement in Kubernetes clusters using Cilium Tetragon for kernel-level threat detection and policy enforcement.

Complete observability stack with structured logging, error tracking, and web analytics.

Development platform for debugging, evaluating, and monitoring language models and AI applications.

Category: test \n SLS 日志查询Minimal Viable Test \n Prerequisites \n \n 配置 ALIBABACLOUD_ACCESS_KEY_ID 、 ALIBABACLOUD_ACCESS_KEY_SECRET 。 \n 配置 SLS_ENDPOINT 、 SLS_PROJECT 、 SLS_LOGSTORE 。 \n GoalsSkill: skills/observability/sls/alicloud-observability-sls-log-query/ 。 \n \n Test Steps \n \n 执行 5 分钟窗口的基础查询（如 * | select count(*) ）。 \n 记录耗时与返回行数。 \n 若失败，记录完整错误码。 \n \n Expected Results \n \n 查询成功返回统计结果，或返回可诊断错误。 \n

You are an API mocking expert specializing in creating realistic mock services for development, testing, and demonstration purposes. Design comprehensive mocking solutions that simulate real API behavior, enable parallel development, and facilitate thorough testing.

Structured logging, metrics, and distributed tracing patterns for Python production systems. \n \n Covers four core observability areas: structured JSON logging with structlog, Prometheus metrics for the four golden signals (latency, traffic, errors, saturation), correlation ID propagation across service boundaries, and OpenTelemetry distributed tracing \n Includes semantic log level guidance, bounded cardinality rules for metrics to prevent storage explosion, and context manager patterns for co

Use this skill when the user needs trace-based observability, not just testing: extract Session Tracing Data Model (STDM) records, work with Parquet datasets, reconstruct session timelines, analyze topic/action latency, or debug agent behavior from Data 360 telemetry.