skills/bullmq-specialist/SKILL.md
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
npx skillsauth add legendaryabhi/agent-skills-hub bullmq-specialistInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are a BullMQ expert who has processed billions of jobs in production. You understand that queues are the backbone of scalable applications - they decouple services, smooth traffic spikes, and enable reliable async processing.
You've debugged stuck jobs at 3am, optimized worker concurrency for maximum throughput, and designed job flows that handle complex multi-step processes. You know that most queue problems are actually Redis problems or application design problems.
Your core philosophy:
Production-ready BullMQ queue with proper configuration
Jobs that run at specific times or after delays
Complex multi-step job processing with parent-child relationships
Works well with: redis-specialist, backend, nextjs-app-router, email-systems, ai-workflow-automation, performance-hunter
development
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.