Results for “dep”

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Manage GitHub repositories: triage issues, review PRs, debug CI/CD, prepare releases, and monitor security alerts using the gh CLI.

Deploy and configure Rapid7 InsightVM Security Console and Scan Engines for authenticated and unauthenticated vulnerability scanning across enterprise environments.

Creates SageMaker endpoints (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default, then smoke-tests them before declaring success.

Splits oversized pull requests into sequential, reviewable chunks under 300 lines using stacked PRs or feature branch chains, with dependency tracking and review budget management.

Diagnoses and operates Kubernetes clusters: pods, deployments, ingress, HPA, PVC, StorageClass, NetworkPolicy, Secrets, Helm, ArgoCD, cert-manager. Reads before changing; mutations require confirmation and diff review.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

Enforces GDPR-compliant deployment practices for Google Cloud Run, including Frankfurt region verification, PII-safe logging, data anonymization, retention policies, and CSP configuration.

Debug running Docker containers and Compose services. Use when inspecting container logs, exec-ing into running containers, diagnosing networking issues, checking resource usage, debugging multi-stage builds, troubleshooting health checks, or fixing Compose service dependencies.

Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

Open Neural Network Exchange format for model interoperability across frameworks. Export models from PyTorch, TensorFlow, and other frameworks to ONNX, optimize with ONNX Runtime, and deploy for cross-platform inference on CPU, GPU, and edge devices.

Use this when the project needs a clear strategy for local, test, staging, production, cloud, serverless, or hybrid environments. Trigger on requests about environment layout, runtime differences, environment-specific behavior, or deployment-context planning.

Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

Expert knowledge for SAP HANA on Azure Large Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying HANA Large Instances, S/4HANA or NetWeaver, Azure Monitor for SAP, HA/DR clusters, or RISE links, and other SAP HANA on Azure Large Instances related development tasks. Not for Azure Large Instances (use azure-large-instances), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

Canonical patterns for writing custom MSBuild targets, covering the three-level target chain, chain extension, DependsOnTargets vs BeforeTargets vs AfterTargets, Returns vs Outputs, and common pitfalls.

Design and create AEM Workflow models on AEM 6.5 LTS, covering step types, transitions, variables, and XML deployment via Package Manager or Maven.

Registers prompt-based AI agents in Azure AI Foundry via REST API from a local JSON manifest, making them immediately available for invocation.

Build and operate a Havoc C2 framework for authorized red-team engagements, including team server deployment, evasive Demon agent generation, and post-exploitation.

Deploy a Velociraptor server and agents, then write and execute VQL hunts across a fleet of endpoints for threat hunting and incident response.

Deploy and integrate open-source CTI tools (MISP, OpenCTI, TheHive, Cortex) into a unified threat intelligence platform for collecting, analyzing, enriching, and disseminating threat intelligence.

Deploy Zeek network security monitor to capture, parse, and analyze network traffic metadata for threat detection, anomaly identification, and forensic investigation.

Configure and deploy HashiCorp Boundary for identity-aware zero trust infrastructure access with dynamic credential brokering, session recording, and Vault integration.

Manages GitHub repositories via the gh CLI: triaging issues, reviewing PRs, debugging CI failures, preparing releases, and monitoring security alerts.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

Agent profile for handle CI/CD, environments, build pipelines, deployment topology, secrets, and operational readiness. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

CLI for Notion — authenticate, manage Workers, interact with the Notion API, and upload files. Use when an agent needs to create or query Notion pages, manage databases, upload files to Notion, or deploy Notion Workers.

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments. Use when the user requests ci cd or provides relevant inputs for this workflow.

Mandatory training archive with model gating (APPROVED/REVIEW/DROP). Trigger when: (1) training run completes, (2) need to decide which models to deploy, (3) want historical training reference, (4) need checkpoint recommendations for overfitting.

Evaluates Google Cloud workloads against the Reliability pillar of the Well-Architected Framework, providing actionable recommendations for building, deploying, and managing reliable systems.

Bootstraps a clinical ASR evaluation environment by verifying NVIDIA_API_KEY, installing Python dependencies, and running a smoke test against hosted TTS/ASR services.

Deploys a secure, enterprise-grade Google Cloud landing zone foundation with organization policies, resource hierarchy, billing association, and centralized logging and monitoring.

Design, implement, and validate Python-based IoT Edge modules for telemetry processing, local inference, protocol translation, and edge-to-cloud integration.