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Found 490 Skills
Migrate a Python application from the classic Elastic APM Python agent to the EDOT Python agent. Use when switching from elastic-apm to elastic-opentelemetry.
Analyzes Claude Code session transcripts (JSONL files) to reveal context window content, token usage patterns, and decision-making processes using view_session_context.py tool. Use when debugging Claude behavior, investigating token patterns, tracking agent delegation, or analyzing context exhaustion. Triggers on "why did Claude do X", "analyze session", "check session logs", "context window exhaustion", or "track agent delegation".
Manage Alibaba Cloud Performance Testing Service (PTS) via OpenAPI/SDK. Use for scene lifecycle operations, test start/stop control, report retrieval, and metadata-driven API discovery before production changes.
Structured JSON logging with correlation IDs, request context propagation across async boundaries, performance timing decorators, and worker metrics collection.
Investigate Kubernetes workload, node, and control-plane issues using OTel telemetry (EDOT). Use when diagnosing pod failures (CrashLoopBackOff, OOMKilled, Error), node pressure, resource exhaustion, image pull failures, admission rejections, autoscaling anomalies, or correlating K8s state with application signals. OTel ingest path only — the legacy ECS Kubernetes integration shape is out of scope.
Run post-deployment smoke checks with Makefile targets (`remote-status`, `remote-logs`) plus optional health URL checks. Use after deployment to verify runtime state before final acceptance.
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.
Parses OpenTelemetry-formatted logs to reconstruct execution traces, extract errors with call chains, and provide AI-powered root cause analysis. Use when investigating errors, checking logs, debugging issues, viewing traces, or analyzing execution flow. Triggers on "check the logs", "analyze errors", "what's failing", "debug this issue", "show me the traces", or "investigate the error".
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards
Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history. Covers 11 MCP tools.
Use production telemetry as INPUT to design new tests. Covers OpenTelemetry integration with tests, trace-based assertions, log-informed test creation, production-error analysis for coverage gaps, and telemetry-driven test prioritization. Use when: "trace-based testing," "design tests from logs," "OpenTelemetry assertions," "production errors point to test gaps," "telemetry-driven testing." Not for: safe rollout techniques (flags, canary) during release — use testing-in-production. Not for: scheduled post-deploy probes — use synthetic-monitoring. Not for: triaging CI failures — use ai-bug-triage. Related: testing-in-production, synthetic-monitoring, qa-metrics, ai-bug-triage.
Wire the current repository through the Caveman Cloud gateway so every LLM request is measured — cost, tokens, latency — with zero behavior change. Use when the user pastes the Caveman setup prompt, says "set up caveman", or wants LLM spend observability added to an app. Requires the gateway URL and a Cave API key (the setup prompt carries both).