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Found 388 Skills
Use when working with error diagnostics smart debug
Exclusive skill set for the GoFrame development framework. Provides comprehensive framework usage guidelines for Go language developers, covering best practices for core components such as command-line management, configuration management, logging components, error handling, data validation, type conversion, cache management, template engines, database ORM, and I18n internationalization. Includes project engineering structure specifications, development mode guidelines, common problem solutions, and rich practical code examples. Suitable for building various Go projects such as RESTful APIs, gRPC microservices, web applications, and CLI tools, helping developers quickly master GoFrame framework features, improve development efficiency and code quality.
Host and process metrics including CPU, memory, disk, network, containers, and process-level telemetry. Monitor infrastructure health and resource utilization.
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, visualizations, markdown documentation, and analytics workflows. Supports notebook creation from scratch, section-based updates, data extraction from Document Store, structure analysis, investigation workflows, and collaborative documentation.
AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services.
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Also handles MCP setup and configuration.
Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy".
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".
Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.
Kubernetes clusters, pods, nodes, workloads, storage, networking, and resource relationships. Query K8s inventory, diagnose degraded deployments and pod failures, investigate rollouts, audit ingress and network policies.
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).
View Langfuse session details with all traces. Use when analyzing conversation flows, checking session costs, or debugging multi-turn interactions.