Results for “kmm”

Manage Kuma service mesh by discovering meshes, dataplanes, and policies via the Kuma API, then analyzing configuration and producing structured reports.

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV or CAM/MCP target discovery, with one-pattern-per-session workflow for scheduler, replication, event listener, HTL lint, dialog, and custom widget migration.

Coordinate agents via the AMQ CLI for file-based inter-agent messaging. Use this skill whenever you need to send messages to another agent (codex, claude, or any named handle), check your inbox, drain queued messages, set up co-op mode between agents, join a swarm team, route messages across projects, or diagnose delivery issues. Also use it when you receive a message and need to know how to reply, inspect receipts, or handle priority. Covers any multi-agent coordination task where agents need to talk to each other — review requests, questions, status updates, decision threads, wake notifications, and orchestrator integration (Symphony, Kanban). For collaborative spec/design workflows specifically, prefer the /amq-spec skill which provides structured phase-by-phase guidance. Not intended for distributed systems design (RabbitMQ, Kafka), CI/CD pipelines, or single-agent tasks with no partner.

Packages and deploys Kubernetes applications with Helm, including charts, templates, and releases.

Runs the NTM operator skill through Codex, using the local shell and returning concrete evidence of commands run and files touched.

Assesses organizational readiness for post-quantum cryptography migration per NIST FIPS 203/204/205 standards, performs cryptographic inventory scanning, evaluates hybrid TLS configurations, and validates CRYSTALS-Kyber and CRYSTALS-Dilithium readiness.

You are an expert in vLLM, the high-throughput LLM serving engine. You help developers deploy open-source models (Llama, Mistral, Qwen, Phi, Gemma) with PagedAttention for efficient memory management, continuous batching, tensor parallelism for multi-GPU, OpenAI-compatible API, and quantization support — achieving 2-24x higher throughput than HuggingFace Transformers for production LLM serving.