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The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose
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Expert knowledge for Azure Quotas development including limits & quotas. Use when checking storage account capacity, IOPS, throughput, portal views, or CLI-based quota increase requests, and other Azure Quotas related development tasks. Not for Azure Monitor (use azure-monitor), Azure Resource Manager (use azure-resource-manager), Azure Policy (use azure-policy).
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Expert knowledge for Azure Managed Lustre development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when sizing AMLustre, integrating Blob, using AKS CSI, setting quotas, or tuning high‑throughput HPC/AI I/O, and other Azure Managed Lustre related development tasks. Not for Azure Blob Storage (use azure-blob-storage), Azure NetApp Files (use azure-netapp-files), Azure Files (use azure-files).
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Optimize NestJS throughput with Fastify adapter, singleton scope enforcement, compression, and query projections. Use when switching to Fastify, diagnosing request-scoped bottlenecks, or profiling API overhead.
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Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.
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Analyze an MSBuild solution, solution filter, or Build.proj that schedules multiple project files. USE FOR: measured idle worker nodes, `-m` that does not scale, a serial ProjectReference critical path, graph builds, or an MSBuild task that builds a Projects list. Requires at least two distinct project files whose scheduling or throughput must be analyzed. DO NOT USE for non-MSBuild build systems.
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Use when you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements. This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput. Part of Plinth Toolkit
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Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.
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Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.
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Python Materials Genomics library for structure analysis, thermodynamics, and electronic properties. Parse/create crystal structures (CIF, POSCAR), query Materials Project for DFT-computed properties, analyze phase and Pourbaix diagrams, compute XRD patterns, generate DFT inputs for VASP, Quantum ESPRESSO, CP2K. Alternatives: ASE (MD/geometry), AFLOW (high-throughput), OVITO (visualization).
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Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.
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Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
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Use FFCV to convert datasets into .beton files, build fast PyTorch loader pipelines, handle image and custom fields/transforms, and tune cache, traversal, and throughput behavior.
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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Fix Monte Carlo project completion simulations that give identical dates for all sub-groups (milestones, priorities, epics). Use when: (1) All sub-group predictions converge to the same date despite different remaining counts, (2) Proportional throughput scaling produces flat/identical results, (3) Applying overall scope rate to individual sub-groups causes simulations to hit max_weeks cap and never converge, (4) Building project forecasting tools that predict completion for sub-groups of a larger backlog. Covers hypergeometric draw model for milestones and cumulative priority model for sequential priorities.
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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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Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For ESM-based analysis, use esm2-sequence-scoring.
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Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
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ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.
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Design and implement caching layers for APIs and web applications using Redis or Memcached. Use when you need to reduce database load, improve response times, or handle traffic spikes. Covers cache-aside, write-through, and write-behind patterns, TTL strategies, cache invalidation, and stampede prevention. Trigger words: cache, Redis, Memcached, TTL, cache invalidation, response time, throughput, rate limiting.
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Build globally distributed apps with Azure Cosmos DB. Work with multiple data models (document, key-value, graph), configure global replication with tunable consistency levels, manage throughput with RU/s, and query with SQL API.
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Patterns for laboratory automation including liquid handling robotics, LIMS integration, protocol development, quality control, and high-throughput workflows. Covers both open-source (Opentrons) and commercial platforms. Use when ", " mentioned.
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Use when training models across multiple GPUs or nodes, handling large models that don't fit in memory, or optimizing training throughput - covers DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointingUse when ", " mentioned.
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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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