256 skills · sorted by relevance
Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.
Use this skill when creating or managing queues, configuring queue membership, setting up case or lead queues, creating public groups, or using groups in sharing rules and manual sharing. Trigger keywords: queue, public group, queue membership, queue email, group sharing, case queue, lead queue. NOT for the rule that decides which queue a record lands in — use admin/assignment-rules. NOT for Omni-Channel routing configuration — use admin/omni-channel-routing-setup.
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Build and review Laravel web applications and APIs with clear separation of responsibilities. Covers Eloquent database models, input validation, consistent API responses, background jobs, events, and caching, helping organize application logic and data access.
Build and test SimPy models of queues, production, logistics, and other event-driven systems. Set execution limits, monitor waiting times and resource use, and analyze repeated runs with recorded random seeds for reproducible results.
Move slow Python operations into background jobs so requests can return promptly. Use task queues and workers, the processes that execute queued work, with retry rules, duplicate handling, time limits, and persistent job status.
Organize human review in Arize by defining label choices, numeric ranges, or free-text feedback fields. Manage review queues and reviewers, submit annotations for individual records, and apply existing labels to project spans, or individual processing records, in bulk.
Work with Amazon SQS for reliable message queuing. Create standard and FIFO queues, configure dead-letter queues for failed messages, send and receive messages in batches, and build decoupled event-driven architectures.
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Use this skill when configuring or troubleshooting Lead or Case assignment rules in Salesforce: creating rule entries, setting filter criteria, assigning records to users or queues, understanding when rules run, and implementing round-robin patterns with Apex. Trigger keywords: lead assignment, case assignment, assignment rule, queue assignment, auto-assign. NOT for creating the queue or public group a rule routes into — use admin/queues-and-public-groups. NOT for Omni-Channel or skills-based routing — use admin/omni-channel-routing-setup. NOT for approval routing — use admin/approval-processes.
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Auto-activate for litestar_queues, QueuePlugin, QueueConfig, WorkerConfig, QueueService, @task, QueuedBackgroundTask, QueueEventsConfig, or SQLSpecBackendConfig. Not for litestar-saq, Celery, or Dramatiq.
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Measure and improve delays in realtime dashboards, data feeds, queues, and caches. Track response times, data age, and waiting work separately, then verify that changes improve the actual behavior of a deployed system.
Guides background processing in Node.js and TypeScript with BullMQ and Redis. Covers job queues, which hold tasks for later execution, along with scheduling, priorities, rate limits, worker concurrency, and dependencies between processing steps.
Plan transactional and marketing email systems with delivery queues, retries, monitoring, and event tracking. Review sender authentication, bounce handling, unsubscribe links, and template practices to address delivery problems and support growing email volumes.
Analyze saved NemoClaw CI timings to rank consistently slow command-line tests and compare base-image publication with reuse. Read-only analyzers report execution times, runner queues, and sample coverage as bounded JSON output.
Create and manage Volcano jobs and scheduling queues in KubeSphere. Use YAML templates for PyTorch, TensorFlow, MPI, and batch workloads, configure resource requests and execution policies, and investigate jobs that remain pending or fail to create pods.
Develop and deploy Cloudflare Workers, manage related storage and services, and troubleshoot configuration with Wrangler. The skill checks official documentation and configuration definitions before preparing commands, with guidance for local development, environments, secrets, and logs.
Helps DevOps engineers configure mirrord Operator's Kafka queue splitting feature end-to-end. Generates the MirrordSplitConfig and MirrordPropertyList Kubernetes CRD YAMLs (the current resources; MirrordKafkaTopicsConsumer + MirrordKafkaClientConfig are deprecated but still supported), the matching mirrord.json split_queues section with message_filter and jq_filter, and Helm value guidance. Use this skill whenever the user mentions Kafka splitting with mirrord, MirrordSplitConfig, MirrordPropertyList, MirrordKafkaClientConfig, MirrordKafkaTopicsConsumer, Kafka queue/topic splitting, configuring mirrord with Kafka, Kafka Streams splitting, MSK IAM auth, or troubleshooting Kafka splitting sessions. Also trigger on split_queues with queue_type Kafka, or connecting mirrord to a Kafka cluster. This is a Team/Enterprise feature of mirrord.
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Helps DevOps engineers configure mirrord Operator's Temporal task queue splitting feature end-to-end. Generates the MirrordSplitConfig and MirrordPropertyList Kubernetes CRD YAMLs, the matching mirrord.json split_queues section with message_filter and jq_filter, and Helm value guidance. Use this skill whenever the user mentions Temporal splitting with mirrord, Temporal task queue splitting, splitting a Temporal worker, configuring mirrord with Temporal or Temporal Cloud, routing Temporal workflow or activity tasks to a local worker, or troubleshooting Temporal splitting sessions. Also trigger on split_queues with queue_type Temporal, or connecting mirrord to a Temporal frontend. This is an alpha Team/Enterprise feature of mirrord.
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Use this skill when the user is doing hands-on NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — those belong to other skills.
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Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: "queue storage", "@azure/storage-queue", "QueueServiceClient", "QueueClient", "send message", "receive message", "dequeue", "visibility timeout".
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Designs and runs the support function — channels, queues, routing, staffing, service levels, quality, and the metrics that show whether it is working. Use this to set up or fix support operations, choose channels, size a team, set or renegotiate service levels, reduce cost per contact, diagnose long queues or poor quality, or decide what to automate.
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AWS SQS message queue service for decoupled architectures. Use when creating queues, configuring dead-letter queues, managing visibility timeouts, implementing FIFO ordering, or integrating with Lambda.
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Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
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Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.
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This skill should be used when the user asks to "set up Cloudflare Queues", "create a message queue", "implement queue consumer", "process background jobs", "configure queue retry logic", "publish messages to queue", "implement dead letter queue", or encountering "queue timeout", "message retry", "throughput exceeded", "queue backlog" errors.
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