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Found 456 Skills
Complete reference for the Portkey AI Gateway Python SDK with unified API access to 200+ LLMs, automatic fallbacks, caching, and full observability. Use when building Python applications that need LLM integration with production-grade reliability.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Implement service mesh (Istio, Linkerd) for service-to-service communication, traffic management, security, and observability.
Expert SRE investigator for incidents and debugging. Uses hypothesis-driven methodology and systematic triage. Can query Axiom observability when available. Use for incident response, root cause analysis, production debugging, or log investigation.
Guidelines for building production-grade microservices with FastAPI/Python and Go, covering serverless patterns, clean architecture, observability, and resilience.
Expert-level monitoring and observability with Prometheus, Grafana, logging, and alerting
Expert guidance for emitting high-quality, cost-efficient OpenTelemetry telemetry. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.
[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res
Analyse Datadog observability data including metrics, logs, monitors, incidents, SLOs, APM traces, RUM, security signals, and more. Use when asked to investigate infrastructure health, query metrics, search logs, check monitors, diagnose errors, or analyse any Datadog data.
Enterprise skill for iOS production error observability and logging (iOS 15+, Swift 5.5+). Use this skill when writing or reviewing error handling code, adding logging to iOS apps, replacing print() with os.Logger, configuring crash reporting SDKs (Sentry, Crashlytics, PostHog), fixing silent error patterns (try?, Task {} swallowing errors, Combine pipelines dying), adding privacy annotations to logs, integrating MetricKit, implementing retry logic with observability, handling errors in SwiftUI .task {} modifiers, or auditing catch blocks for proper error reporting. Use this skill any time someone writes a catch block, uses try?, creates a Task {}, sets up error handling, or mentions logging, crash reporting, or error tracking in an iOS context — even if they just say 'add error handling' or 'why is this failing silently.'
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.