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DevOps & Cloud
Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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Showing 769–792 of 17,845 skills
- Altinity Expert Clickhouse MetricsReal-time monitoring of ClickHouse metrics, events, and asynchronous metrics. Use for load average, connections, queue monitoring, and resource saturation.Votes: 0GitHub stars: 2
- Alibaba CloudProvides comprehensive Alibaba Cloud (Aliyun) guidance including ECS, ApsaraDB, OSS, SLB, VPC, RAM, ACK (Kubernetes), Function Compute, API Gateway, CDN, and monitoring services. Covers infrastructure provisioning with Terraform/ROS, cloud architecture design, security best practices, cost optimization, and migration strategies. Produces infrastructure code, deployment scripts, architecture diagrams, and operational procedures. Use when working with Alibaba Cloud services, designing cloud arc...Votes: 0GitHub stars: 2
- Algorithmic Art 8Create generative art using p5.js with seeded randomness and interactive exploration. Use for computational aesthetics, parametric design, particle systems, noise fields, and procedural generation.Votes: 0GitHub stars: 2
- Alertmanager InstallerInstall and configure AlertManager following monitoring guide patterns and best practices for Kubernetes environments. Trigger with /alertmanager-installVotes: 0GitHub stars: 2
- AirflowManages Apache Airflow operations including listing, testing, running, and debugging DAGs, viewing task logs, checking connections and variables, and monitoring system health. Use when working with Airflow DAGs, pipelines, workflows, or tasks, or when the user mentions testing dags, running pipelines, debugging workflows, dag failures, task errors, dag status, pipeline status, list dags, show connections, check variables, or airflow health.Votes: 0GitHub stars: 2
- Airflow DagApache Airflow DAG development with TaskFlow API, Google Cloud operators (BigQuery, GCS), dbt integration, and dynamic DAG generation. Use when creating or modifying Airflow DAGs, implementing data pipeline orchestration, setting up cross-DAG dependencies with ExternalTaskSensor, adding deferrable operators, or configuring error handling and retries.Votes: 0GitHub stars: 2
- Aind InfrastructureKnowledge about AIND data infrastructure including MongoDB/DocumentDB access patterns, S3 asset storage, collection schemas, and query patterns. Use when working with AIND analysis results, querying metadata, or accessing stored assets.Votes: 0GitHub stars: 2
- Aiml OperationsThis skill should be used when the user asks to "deploy ML model", "MLOps", "model serving", "feature store", "experiment tracking", "model registry", "ML pipeline", "model monitoring", "GPU inference", "TensorFlow Serving", "MLflow", or needs help with machine learning operations and model deployment.Votes: 0GitHub stars: 2
- Project ContextProvides current project state, architecture overview, and structured metadataVotes: 0GitHub stars: 2
- Ai MlopsComplete MLOps skill covering production ML lifecycle and security. Includes data ingestion, model deployment, drift detection, monitoring, plus ML security (prompt injection, jailbreak defense, RAG security, privacy, governance). Modern automation-first patterns with multi-layered defenses.Votes: 0GitHub stars: 2
- Ai Ml TimeseriesOperational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.Votes: 0GitHub stars: 2
- Ai Llm InferenceOperational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving at scale. Emphasizes production-grade performance, cost control, and observability.Votes: 0GitHub stars: 2
- Ai Error LearnerCatalogs recurring errors as pain points needing skills. Use when same error occurs multiple times.Votes: 0GitHub stars: 2
- Trading Session TimezonesTrading session timing for MES/MGC futures with timezone handling. Use when implementing session-based entry/exit logic, ORB time windows, RTH detection, or handling global market overlaps.Votes: 0GitHub stars: 2
- Trading Code ReviewPre-live deployment checklist for NinjaTrader trading strategies. Use before deploying any code changes to live trading, when reviewing strategy code for critical bugs, or verifying Apex compliance and WSGTA trading rules.Votes: 0GitHub stars: 2
- Opus Deployment GuideDeployment options for Opus-generated code in Antigravity IDE. Includes automatic MCP deployment, Haiku fallback, manual copy/paste, and instructions for each IDE setup.Votes: 0GitHub stars: 2
- Nt8 Log MonitorAutonomous monitoring of NinjaTrader 8 logs and trace files for errors, rejections, and crashes.Votes: 0GitHub stars: 2
- Apex Rithmic TradingApex Trader Funding account compliance and Rithmic data feed optimization for NinjaTrader 8. Use when implementing daily loss limits, trailing drawdown monitoring, order rate-limiting, Rithmic disconnect detection, or ensuring Apex account compliance.Votes: 0GitHub stars: 2
- Agnosticv ValidatorValidate AgnosticV catalog configurations against best practices and deployment requirementsVotes: 0GitHub stars: 2
- Agnosticv Catalog BuilderCreate or update AgnosticV catalog files (common.yaml, dev.yaml, description.adoc, info-message-template.adoc)Votes: 0GitHub stars: 2
- AgentsManage and develop AI agents using kubani-dev CLI. Use for checking agent health, versions, deployment status, running tests, and evaluations.Votes: 0GitHub stars: 2
- Agentio GchatUse when interacting with Google Chat - send messages, list messages, or get message details. Requires agentio CLI with a configured Google Chat profile (webhook or OAuth).Votes: 0GitHub stars: 2
- Agentdb Performance OptimizationOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.Votes: 0GitHub stars: 2
- Agentdb Advanced FeaturesMaster advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.Votes: 0GitHub stars: 2