Results for “workloads”

Routes user requests to specialist skills for AEM 6.5 LTS workflow model design, custom step development, launcher configuration, triggering, debugging, and triaging.

Web Workers

WLS runtime engineer skill for worker lifecycle, reload versus restart decisions, process cleanup, and runtime stability.

Use to staff and schedule QA reviewers for sustainable throughput without grading quality collapsing — fatigue, drift, and false precision from treating reviewers like production agents. Trigger for "how many QA reviewers do we need", reviewer capacity planning, grader fatigue, throughput per reviewer, calibration after long shifts, or scores drifting over the course of a day.

Design, build, and manage AWS Step Functions state machines for serverless workflow orchestration, including error handling, parallel execution, and integration with AWS services.

当需要系统性地提升工作专注度和生产力，实现高质量成果时

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.

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.

Frame jobs, triggers, desired outcomes, anxieties, and product opportunities.

Databricks Job & Notebook Lineage (Lite)

Use this when background workers or job processors need design or review. Trigger on requests about asynchronous processing, job payloads, retries, scheduling, worker ownership, or long-running tasks outside the request path.

Long Tasks

Build serverless background jobs, event-driven workflows, and durable execution with Inngest, without managing queues or workers.

Scales Python ML workloads across clusters using Ray's distributed tasks, actors, data, and serving capabilities.

Load a written implementation plan, execute each task with verification checkpoints, and complete development using a structured workflow.

Assesses and automates upgrades of Azure workloads between plans, tiers, or SKUs, and modernizes Azure SDK for Java dependencies in source code.

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.