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Found 228 Skills
Cloud Logging Sink Setup - Auto-activating skill for GCP Skills. Triggers on: cloud logging sink setup, cloud logging sink setup Part of the GCP Skills skill category.
Fix a specific bug or problem in the codebase. Supports two modes - immediate fix or plan-first. Without arguments executes existing FIX_PLAN.md. Always suggests test coverage and adds logging. Use when user says "fix bug", "debug this", "something is broken", or pastes an error message.
AdvantageKit logging framework best practices for FRC Java robots (2026 / AKit 4.x). Use when implementing or reviewing AdvantageKit IO layers, Logger usage, replay-compatible subsystem design, signal logging, output logging, or deterministic simulation. Triggers on: AdvantageKit, Logger.recordOutput, Logger.processInputs, LoggedRobot, IO interfaces, IOInputs, AutoLog annotation, replay, log-replay, non-deterministic, or any AdvantageKit-related robot code task.
Claude Code hooks configuration specialist. Use when creating hooks for tool validation, logging, notifications, or custom automation in Claude Code.
Troubleshoot and resolve issues with Azure Messaging SDKs for Event Hubs and Service Bus. Covers connection failures, authentication errors, message processing issues, and SDK configuration problems. USE FOR: event hub SDK error, service bus SDK issue, messaging connection failure, AMQP error, event processor host issue, message lock lost, send timeout, receiver disconnected, SDK troubleshooting, azure messaging SDK, event hub consumer, service bus queue issue, topic subscription error, enable logging event hub, service bus logging, eventhub python, servicebus java, eventhub javascript, servicebus dotnet, event hub checkpoint, event hub not receiving messages, service bus dead letter DO NOT USE FOR: creating Event Hub or Service Bus resources (use azure-prepare), monitoring metrics (use azure-observability), cost analysis (use azure-cost-optimization)
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
Review AI API key leakage patterns and redaction strategies. Use for identifying exposed keys for OpenAI, Anthropic, Gemini, and 10+ other providers. Use proactively when code integrates AI providers or when environment variables/keys are present. Examples: - user: "Check for leaked OpenAI keys" → scan for `sk-` patterns and client-side exposure - user: "Is my Gemini integration secure?" → audit vertex AI config and key redaction - user: "Review AI provider logging" → ensure secrets are redacted from logs - user: "Scan for Anthropic secrets" → check for `ant-` keys in code and configs - user: "Audit Vertex AI integration" → verify proper IAM roles and service account usage
Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
Version-aware guide for configuring and running Apollo Router for federated GraphQL supergraphs. Generates correct YAML for both Router v1.x and v2.x. Use this skill when: (1) setting up Apollo Router to run a supergraph, (2) configuring routing, headers, or CORS, (3) implementing custom plugins (Rhai scripts or coprocessors), (4) configuring telemetry (tracing, metrics, logging), (5) troubleshooting Router performance or connectivity issues.
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.