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HighThroughput LLMS

serving-llms-vllm

Orchestra-Research/AI-Research-SKILLs

Deploy LLMs with vLLM to maximize throughput and minimize latency through PagedAttention, continuous batching, quantization, and tensor parallelism for production APIs or batch inference on constrained GPUs.

Sprint Execution Workflow

sprint-workflow

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through the Sprint plugin’s convergent diffusion model, covering phases from loading specs to finalization with agent orchestration, parallel implementation, testing, and review so users understand when and how to run sprints.

Test Parallelizer Guide

test-parallelizer

jeremylongshore/claude-code-plugins-plus-skills

Automates help with test parallelizer workflows, offering guided best practices, configuration tips, and code snippets for parallelized unit and integration testing scenarios.

Megatron LLM Training

training-llms-megatron

Orchestra-Research/AI-Research-SKILLs

Megatron-Core orchestrates training of 2B–462B parameter LLMs with tensor/pipeline/context/expert parallelism, maximizing H100 MFU for production-grade distributed training pipelines.

Temporal Workflow Patterns

workflow-orchestration-patterns

sickn33/antigravity-awesome-skills

Guides engineers through Temporal workflow orchestration architecture, covering deterministic workflows vs activities, saga compensation, entity workflows, parallel execution, async callbacks, and best practices for resilient distributed systems.

Distributed Dask Workflows

K-Dense-AI/claude-scientific-skills

Dask enables larger-than-memory pandas/NumPy workflows through parallel, distributed execution across dataframes, arrays, bags, and futures, making it ideal for scaling ETL, analytics, or custom computations across cores or clusters.

Compute Resource Monitor

get-available-resources

K-Dense-AI/claude-scientific-skills

Assesses available CPU, GPU, memory, and disk before computationally intensive scientific work, emitting a JSON summary with strategic recommendations on parallelism, memory handling, and GPU backends so you can choose the optimal compute pattern.

Medchem Filtering Toolkit

K-Dense-AI/claude-scientific-skills

Medchem is a Python library for applying drug-likeness rules, structural alerts, and complexity metrics to triage compound libraries. Run rule sets, catalogs, and custom constraints with parallel filtering, aiding lead optimization and quality control workflows.

Molecular Featurization Hub

K-Dense-AI/claude-scientific-skills

Molfeat is a Python hub for turning SMILES or RDKit molecules into 100+ featurizers and pretrained embeddings, powering QSAR, screening, similarity, and deep learning workflows with transformers, calculators, caching, and parallel processing.

Polars DataFrame Engine

K-Dense-AI/claude-scientific-skills

Lightning-fast Arrow-based DataFrame library for Python and Rust that uses lazy evaluation and parallel execution to power 1-100GB ETL pipelines as a faster pandas replacement.

PufferLib Reinforcement Learning

K-Dense-AI/claude-scientific-skills

PufferLib is a high-performance RL framework for fast parallel training, vectorized environments, and multi-agent workflows; use it when you need million-step-per-second PPO training, custom environments, or integration with Gymnasium, Atari, Procgen, and other ecosystems.

Research Lookup Router

research-lookup

K-Dense-AI/claude-scientific-skills

Automatic research lookup routing that picks Parallel Chat API for general inquiries and Perplexity sonic search for academic papers, making it ideal for finding studies, verifying claims, gathering market data, or sourcing citations for documentation.

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