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Found 456 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.
Use when deploying Docker Compose applications to production including security hardening, resource management, health checks, logging, monitoring, and high-availability patterns.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
Real User Monitoring (RUM), Web Vitals, user sessions, mobile crashes, page performance, user interactions, and frontend errors. Query web and mobile frontend telemetry.
Log queries, filtering, pattern analysis, and log correlation. Search and analyze application and infrastructure logs.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use developing-agentforce); writes or runs test specs (use testing-agentforce); uses sf agent preview for local development iteration; deploys or publishes agents.
Distributed traces, spans, service dependencies, performance analysis, and failure detection. Query trace data, analyze request flows, and investigate span-level details.
Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.
Author monitoring resources: PrometheusRules, ServiceMonitors, PodMonitors, AlertmanagerConfig, Silence CRs, and canary-checker health checks. Use when: (1) Creating or modifying alert rules (PrometheusRule), (2) Adding scrape targets (ServiceMonitor/PodMonitor), (3) Configuring Alertmanager routing or silences, (4) Writing canary-checker health checks, (5) Creating recording rules, (6) Adding monitoring for a new application or platform component. Triggers: "create alert", "add alerting", "PrometheusRule", "ServiceMonitor", "PodMonitor", "AlertmanagerConfig", "silence alert", "canary check", "recording rule", "add monitoring", "scrape target", "alert rule", "prometheus rule", "health check canary"
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.