12,240 skills found · Page 293 of 408
BagelHole / docker-managementBuild, optimize, and troubleshoot Docker containers and images. Create efficient Dockerfiles, manage container lifecycle, configure networking and volumes, and debug container issues
BagelHole / podmanManage containers using Podman, the daemonless container engine. Run rootless containers, create pods, manage images, and use Docker-compatible commands
BagelHole / devcontainers-nixCreate reproducible development environments with Dev Containers, Nix flakes, and Devbox for consistent toolchains across teams
BagelHole / datadogImplement Datadog monitoring and APM for infrastructure and applications. Configure agents, create dashboards, set up alerts, and implement distributed tracing
BagelHole / elk-stackDeploy and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis. Configure log pipelines, create visualizations, and implement log-based monitoring
BagelHole / loki-loggingConfigure Grafana Loki for log aggregation and analysis. Set up Promtail for log collection, write LogQL queries, and integrate with Grafana for visualization
BagelHole / new-relicConfigure New Relic observability platform for infrastructure and application monitoring. Set up APM agents, create dashboards, configure alerts, and implement distributed tracing
BagelHole / prometheus-grafanaSet up metrics collection and visualization with Prometheus and Grafana. Configure scrape targets, create PromQL queries, build dashboards, and implement alerting
BagelHole / argocd-gitopsImplement GitOps with ArgoCD for declarative Kubernetes deployments. Configure applications, manage sync policies, implement progressive delivery, and automate deployments from Git repositories
BagelHole / helm-chartsCreate, manage, and deploy Helm charts for Kubernetes package management. Build reusable chart templates, manage releases, configure values, and use Helm repositories
BagelHole / kubernetes-opsDeploy, scale, and manage Kubernetes workloads. Create deployments, services, and configurations, manage cluster resources, troubleshoot pods, and implement production-ready Kubernetes patterns
BagelHole / kustomizeCustomize Kubernetes manifests without templating using Kustomize. Create base configurations with environment overlays, manage configuration variants, and patch resources declaratively
BagelHole / model-serving-kubernetesDeploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
BagelHole / openshiftManage Red Hat OpenShift clusters and deployments. Configure projects, routes, builds, and deploy applications using OpenShift-specific features
BagelHole / blue-green-deployConfigure zero-downtime deployment strategies including blue-green, canary, and rolling deployments. Implement traffic shifting, health checks, and rollback procedures
BagelHole / feature-flagsImplement feature flags for progressive feature rollout using LaunchDarkly, Unleash, or custom solutions. Control feature visibility, perform A/B testing, and enable trunk-based development
BagelHole / git-workflowImplement Git branching strategies, PR workflows, and release management patterns. Configure GitFlow, trunk-based development, or GitHub Flow for team collaboration
BagelHole / semantic-versioningAutomate versioning and changelog generation using semantic versioning principles. Configure release automation, version bumping, and changelog tools
BagelHole / vector-database-opsDeploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
BagelHole / llm-fine-tuningSet up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
BagelHole / llm-inference-scalingAuto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.
BagelHole / rag-infrastructureBuild and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
BagelHole / vllm-serverDeploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.
BagelHole / ai-inference-service-meshUse service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
BagelHole / llm-gatewayDeploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking. Covers LiteLLM Proxy, OpenRouter-compatible setup, and custom Nginx/Traefik patterns.
BagelHole / vercel-deploymentsDeploy frontend and full-stack apps on Vercel with previews, edge functions, environment promotion, and production guardrails
BagelHole / gpu-server-managementSet up and manage NVIDIA GPU servers for AI workloads — driver installation, CUDA toolkit, container toolkit, MIG partitioning, GPU health monitoring, and multi-GPU configuration for LLM inference and training.
BagelHole / ai-security-hardeningHarden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.
BagelHole / zero-trustImplement zero-trust network architecture. Configure identity-based access, micro-segmentation, and continuous verification
BagelHole / container-scanningScan container images for vulnerabilities using Trivy, Grype, and cloud-native tools. Identify security issues in base images, packages, and configurations
