Skills for “observability”

…up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace,…

…AI gateway, LLM calls, realtime, logs, Loki, Grafana, observability, postgres, database, backend, Claimable Neon, neon.new, a no-signup database, or pairing Neon with Upstash for cache, Redis, rate limiting,…

No description in the SKILL.md.

First principles of observability: wide events, high cardinality, the core analysis loop, events vs metrics vs logs, and how instrumentation connects to debugging. Trigger phrases: "what is observability", "why…

Sets up CloudWatch observability for the first time - Omni (CloudWatch Application Observability) and classic CloudWatch. Omni: creating a Space or Domain; access grants (who has access, at what level), access profiles…

…insights; builds, configures, debugs, tunes AWS observability - operator-symptom questions, detecting Omni vs classic CloudWatch. CloudWatch on a reporting service: fleet health views, Log Insights,…

Signals scout for PostHog AI observability. Watches LLM traces for cost, latency, error, volume, and eval-performance regressions.

Signals scout for observability gaps — significant event volumes with no insight, dashboard, or alert coverage. Recommends new insights, dashboards, or alerts as the product evolves.

Redis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT…

…skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including raw ATOF events, ATIF trajectories, OpenTelemetry, OpenInference, or custom…

Monitors, inventories, and assesses Google Cloud Managed Service for Apache Spark (Dataproc Serverless and Dataproc on Compute Engine) environments, clusters, batches, jobs, sessions, metrics, logs, costs, and…

Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is…

Surface AIDP operational state — recently accessed resources and long-running async operations (status, completion, waiting). Use when the user asks "what happened recently", "what's running", "is that operation done…

…set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why…

Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI…

…traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when the user says "add tracing/observability/monitoring to my agent", wants to instrument an existing agent project in…

…to forward LLM and API spend to your own observability system. Use when adding cost/spend logging, building a cost observability integration, or forwarding per-request cost data to an external…

…DCU/cost consumption, idle/zombie clusters, or workload observability (use @skill:gcp-spark-observability). - Troubleshooting failed Spark jobs or Dataproc batches (use…

Add Pydantic Logfire observability to application code — traces, logs, metrics, and AI/agent spans. Use when the user asks to add or configure Logfire, observability, tracing, logging, or monitoring; maximize useful…

…Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly,…