Loading...
Loading...
Found 472 Skills
End-to-end pipeline from unlabeled ml_app traces to a bootstrapped evaluator suite. Runs trace classification → root cause analysis → eval bootstrap in sequence with user checkpoints. Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", or wants a guided walkthrough from production data to evaluator code.
Expert-level Prometheus monitoring, metrics collection, PromQL queries, alerting, and production operations
Prometheus-compatible metrics collection with counters, gauges, and histograms. Export metrics for dashboards and alerts with proper labeling.
Full Sentry SDK setup for Android. Use when asked to "add Sentry to Android", "install sentry-android", "setup Sentry in Android", or configure error monitoring, tracing, profiling, session replay, or logging for Android applications. Supports Kotlin and Java codebases.
Integrates Kelet into AI applications end-to-end: instruments agentic flows with OTEL tracing, maps session boundaries, adds user feedback signals (VoteFeedback, edit tracking, coded behavioral hooks), generates synthetic signal evaluator deeplinks, and verifies the integration. Kelet is an AI agent that performs Root Cause Analysis on AI app failures — it ingests traces and signals, clusters failure patterns, and suggests fixes. Use when the developer mentions Kelet or asks to integrate, set up, instrument, or add tracing/signals/feedback to their AI app. Triggers on: "integrate Kelet", "set up Kelet", "add Kelet", "instrument my agent", "connect Kelet", "use Kelet".
Service metrics, RED metrics (Rate, Errors, Duration), and runtime-specific telemetry for .NET, Java, Node.js, Python, PHP, and Go applications.
Datadog integration. Manage Monitors, Dashboards, Incidents, Notebooks, Logs, Metrics and more. Use when the user wants to interact with Datadog data.
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).
Next.js/Vercel OpenTelemetry style: instrumentation.ts, @vercel/otel bootstrap, native @opentelemetry/api call sites, inline endpoint + ingest key, and no raw NodeSDK replacement.
Onboard a project to Superlog by installing OpenTelemetry traces, logs, and metrics across every app and service in the repo. Triggers on requests like 'install Superlog', 'set up Superlog', 'add Superlog telemetry', 'onboard this repo to Superlog', 'instrument with OpenTelemetry for Superlog'.
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
Create, update, and manage Slot deployments for Katana and Torii services.