Results for “dep”

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Assists with building secure JavaScript and TypeScript applications using the Deno runtime. Use when creating servers, CLI tools, or scripts with Deno's built-in tooling, permission model, and web standards APIs. Trigger words: deno, deno deploy, deno serve, deno kv, deno permissions, secure runtime, jsr.

Drafts formal responses to governmental Notices of Violation, covering acknowledgment, factual timeline, legal defenses, mitigation, and proposed corrective action. Use when responding to environmental enforcement actions, regulatory citations, compliance violations, or governmental NOVs from agencies including EPA, OSHA, state environmental departments, or local code enforcement.

OpenStack Heat orchestration service skill. Use when working with HOT templates, stack lifecycle management, auto-scaling groups, nested stacks, resource type registry, template validation, or infrastructure-as-code patterns within OpenStack. Covers deployment via Kolla-Ansible, template authoring, stack operations, and troubleshooting common orchestration failures.

Host security hardening and risk-tolerance configuration for OpenClaw deployments. Use when a user asks for security audits, firewall/SSH/update hardening, risk posture, exposure review, OpenClaw cron scheduling for periodic checks, or version status checks on a machine running OpenClaw (laptop, workstation, Pi, VPS).

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

Evaluates Google Cloud workloads against the Cost Optimization pillar of the Well-Architected Framework, identifies cost requirements and constraints, and provides actionable recommendations for building, deploying, and managing workloads cost-efficiently.

Deploys and configures Zeek network security monitor to passively analyze traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and incident response.

Deploys canary tokens and honeytokens (fake AWS credentials, DNS canaries, document beacons, database records) that trigger alerts when accessed by attackers. Uses the Canarytokens API and custom webhook integrations for breach detection.

Audit Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.

Enforce organizational security policies across Kubernetes clusters and CI/CD pipelines using Open Policy Agent (OPA) and Gatekeeper, including writing Rego policies, deploying admission controllers, and testing policies locally.

Deploy Zero Trust Network Access using Zscaler Private Access (ZPA) to replace traditional VPN with identity-based, context-aware access to private applications through the Zscaler Zero Trust Exchange.

Configure and deploy Palo Alto Networks next-generation firewalls with App-ID, User-ID, zone-based policies, SSL decryption, and threat prevention profiles for enterprise network security.

Deploy CyberArk Privileged Access Management to discover, vault, rotate, and monitor privileged credentials across enterprise infrastructure, covering vault architecture, session isolation, credential rotation policies, and NIST 800-53 integration.

Implement signal-based reactive state management in Angular v20+. Use for creating reactive state with signal(), derived state with computed(), dependent state with linkedSignal(), and side effects with effect(). Triggers on state management questions, converting from BehaviorSubject/Observable patterns to signals, or implementing reactive data flows.

Full end-to-end Mushi Mushi integration smoke test: bug capture → AI triage → story mapping → TDD test generation → approval → execution → PDCA cycle. Use when "test mushi integration", "verify full pipeline", "mushi e2e check", "does mushi work end-to-end", "smoke test mushi", or after deploying changes.

Expert API designer for REST, GraphQL, gRPC architectures. Activate on: API design, REST API, GraphQL schema, gRPC service, OpenAPI, Swagger, API versioning, endpoint design, rate limiting, OAuth flow. NOT for: database schema (use data-pipeline-engineer), frontend consumption (use web-design-expert), deployment (use devops-automator).

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

Review pull requests or audit repositories for correctness, security, performance, test risk, architecture, dependencies, developer experience, documentation, and grounded product direction. Use for code review, repository audits, evidence-backed findings, false-positive vetting, risk assessment, prioritization, or deciding which improvements should become implementation plans.

Runs Python code in the cloud with Modal — serverless containers, on-demand GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that need GPU acceleration or dynamic scaling. Part of the AlterLab Academic Skills suite.

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.

Deploy and configure Tofino industrial firewalls to protect SCADA systems and PLCs using deep packet inspection for OT protocols including Modbus, EtherNet/IP, OPC, and S7comm, enforcing granular access control between ICS security zones.

Design and enforce microsegmentation policies using workload identity and label-based rules to prevent lateral movement in zero trust architectures, with guidance for tools like VMware NSX, Illumio, and Calico.

Deploy AWS Config rules for continuous compliance monitoring, including managed and custom rules aligned to CIS and PCI DSS frameworks, automatic remediation with SSM Automation, and multi-account compliance aggregation.

Deploy Cisco Duo multi-factor authentication across enterprise applications, VPN, RDP, and SSH access points, covering integration methods, adaptive policies, device trust, and phishing-resistant MFA aligned with NIST 800-63B.

Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring.

Classifies a repository into one of six archetypes and installs a curated bundle of agent skills covering build, test, review, deploy, and docs, with a user-visible manifest and a recorded stack file.

Reuse a running Chrome session for browser automation via Playwriter CLI + MCP. Use when the task depends on the browser the user already has open — existing logins, cookies, extensions, passkey-friendly flows, or live-tab continuity. Route repeatable fresh-browser or CI-style checks to `browser-harness` instead.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.