Results for “greenbone”

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Hardens Google Kubernetes Engine (GKE) clusters with Workload Identity, Secret Manager, RBAC, Binary Authorization, Network Policies, and Pod Security Standards.

Plan and apply safe, reversible headless-mode changes on Jetson devices to reclaim memory from the GUI and non-essential daemons.

Routes to specialized GKE sub-skills for cluster management, networking, security, scaling, and more on Google Kubernetes Engine.

Configures GKE workload reliability with PodDisruptionBudgets, health probes, topology spread constraints, and graceful shutdown patterns.

Plans, configures, and manages GKE networking including private clusters, VPC-native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning.

Infer gene regulatory networks (GRNs) from expression matrices using arboreto's scalable GRNBoost2 and GENIE3 tree-ensemble algorithms with Dask-distributed computation. Use when analyzing bulk or single-cell RNA-seq transcriptomics to map transcription-factor-to-target-gene regulatory interactions, build adjacency networks, or run the GRN-inference step of a SCENIC pipeline on large datasets. Part of the AlterLab Academic Skills suite.

Operate and validate a GoHome service via gRPC discovery, read-only RPC calls, and Prometheus metrics exposed over HTTP.

Operates GoHome services via gRPC discovery, metrics validation, and read-only RPC calls, with stateful actions gated on user approval.

Designs and implements blue-green deployment architectures with environment switching, traffic routing, automated rollback, and health monitoring for zero-downtime releases.

Operate GoHome services via gRPC discovery, metrics validation, and read-only RPC calls, with stateful actions gated on user approval.

Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.

Streams change data capture from Oracle Database into Redpanda or Kafka using the oracledb_cdc input in Redpanda Connect, which reads redo logs via LogMiner. Covers configuration, Oracle setup, checkpointing, and enterprise features.

Diagnoses and validates Redpanda Connect pipelines using linting, dry-run connection tests, logging, metrics, tracing, and health endpoints, including enterprise feature troubleshooting.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Diagnoses Redpanda broker and cluster health using debug bundles, metrics endpoints, logs, CPU profiling, and partition/raft checks, with triage playbooks for common failure modes.

Patterns for debugging and extending the AegisNex GitHub Actions CI pipeline. Covers common failure modes, encoding issues, dependency problems, and how to add new CI steps.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.