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Found 223 Skills
Scheduled function patterns for background tasks including interval scheduling, cron expressions, job monitoring, retry strategies, and best practices for long-running tasks
Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.
Unit tests for scheduled and async tasks using @Scheduled and @Async. Mock task execution and timing. Use when validating asynchronous operations and scheduling behavior.
Complete development skill set for the ABE Framework, providing a full-stack solution for modern Go HTTP RESTful API application development. Core features include: modular engine architecture, standardized controller route registration, global and route-level middleware system, dependency injection container (supporting global and request-level scopes), multi-language internationalization (i18n) support, access control system based on JWT and Casbin, asynchronous event bus mechanism, high-performance goroutine pool management, extensible plugin mechanism, configuration management system (supporting multi-layer configuration priority), GORM database integration, structured logging system, form validation framework, scheduled task scheduling (Cron), CORS cross-domain support, etc. Suitable for scenarios such as building enterprise-level web services, microservice architecture applications, API gateways, and backend management systems. The framework adopts a loose-coupling design, supports the UseCase business logic pattern, provides a complete error handling mechanism and performance monitoring capabilities, helping enterprises quickly build stable and maintainable distributed application systems.
Gives the agent the ability to send, receive, search, and manage emails directly from the terminal or via a local HTTP API. Use this skill when the agent needs to handle email tasks: sending messages, reading inbox, replying, forwarding, managing contacts, organizing with tags/folders/filters, scheduling background sync, setting up webhooks for new email events, or automating email workflows. Supports structured output (--format json/markdown/html), field selection (--fields), standardized exit codes, and a local REST API with OpenAPI docs. Works with IMAP/SMTP providers including Gmail, Outlook, QQ Mail, and others. Activates on keywords: send email, check inbox, reply, forward, email automation, contacts, email template, notifications, webhook, http api, format json, field selection, serve, openapi.
Implement Syncfusion WPF Scheduler (SfScheduler) for managing appointments and calendar views in desktop applications. Use this when building scheduling interfaces, appointment management systems, or resource booking applications. This skill covers calendar views, appointment handling, resource scheduling, timeline customization, and Outlook-style calendar functionality.
Builds and deploys data processing and ML training pipelines using TrueFoundry Workflows (built on Flyte). Use when creating DAGs, orchestrating multi-step tasks, scheduling ETL pipelines, or running ML training workflows.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
Guidance for implementing PyTorch pipeline parallelism for distributed model training. This skill should be used when tasks involve implementing pipeline parallelism, distributed training with model partitioning across GPUs/ranks, AFAB (All-Forward-All-Backward) scheduling, or inter-rank tensor communication using torch.distributed.
Expert in background jobs and message queues using Gravito Quasar. Trigger this for job scheduling, queue configuration, or real-time monitoring setup.
Query grid electricity forecasts and submit load events using EnergyKit to help users optimize home electricity usage. Use when building smart home apps, EV charger controls, HVAC scheduling, or energy management dashboards that guide users to use power during cleaner or cheaper grid periods.