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Found 224 Skills
Configures .NET CI/CD pipelines (GitHub Actions with setup-dotnet, NuGet cache, reusable workflows; Azure DevOps with DotNetCoreCLI, templates, multi-stage), containerization (multi-stage Dockerfiles, Compose, rootless), packaging (NuGet authoring, source generators, MSIX signing), release management (NBGV, SemVer, changelogs, GitHub Releases), and observability (OpenTelemetry, health checks, structured logging, PII). Spans 18 topic areas. Do not use for application-layer API or UI implementation patterns.
Axiom observability API for logs and analytics. Use when user mentions "logs", "query logs", "Axiom", or asks about event analytics.
Expert-level monitoring and observability with Prometheus, Grafana, logging, and alerting
Implement OpenAI Harness Engineering practices in any repository. Use when setting up or refactoring agent-first workflows, writing or upgrading AGENTS.md and PLANS.md, creating deterministic smoke/test/lint/typecheck harness commands, defining strict architecture boundaries and data-shape contracts, wiring observability from day 1, and adding entropy-control checks plus CI automation for reliable autonomous runs.
Instrument a .NET application with the Elastic Distribution of OpenTelemetry (EDOT) .NET SDK for automatic tracing, metrics, and logs. Use when adding observability to a .NET service that has no existing APM agent.
Vercel Observability expert guidance — Drains (logs, traces, speed insights, web analytics), Web Analytics, Speed Insights, runtime logs, custom events, OpenTelemetry integration, and monitoring dashboards. Use when instrumenting, debugging, or optimizing application performance and user experience on Vercel.
Instrument a Java application with the Elastic Distribution of OpenTelemetry (EDOT) Java agent for automatic tracing, metrics, and logs. Use when adding observability to a Java service that has no existing APM agent.
Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
Lead complex software implementation, architecture decisions, and reliable delivery across any modern technology stack. Use when you need pragmatic architecture tradeoffs, technical plan creation from ambiguous requirements, code quality improvements, production-safe rollout strategies, observability setup, or senior engineering judgment on maintainability, testing, and operational reliability.
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.