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Neural Network Builder

building-neural-networks

jeremylongshore/claude-code-plugins-plus-skills

Enables Claude to configure neural network architectures through the neural-network-builder plugin, creating or modifying models and defining layers, activation functions, and training parameters whenever network design or tuning is requested.

Feature Engineering Toolkit

engineering-features-for-machine-learning

jeremylongshore/claude-code-plugins-plus-skills

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

GPTQ LLM Quantization

Orchestra-Research/AI-Research-SKILLs

Post-training 4-bit quantization for LLMs that trims memory by 4× with minimal perplexity loss, enabling 70B+ models to run faster on consumer GPUs while integrating with transformers, PEFT, and QLoRA workflows.

Fast NLP Tokenizers

huggingface-tokenizers

Orchestra-Research/AI-Research-SKILLs

Rust-powered HuggingFace tokenizers deliver <20s per GB speed, trainable BPE/WordPiece/Unigram models, alignment tracking, padding, and transformers integration for production NLP pipelines.

Lindy Migration Deep Dive

lindy-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through migrating complex AI agents into Lindy with scope assessment, consolidation, phased rollouts, data transforms, rollback checks, and validation checklists for production readiness.

Long Context Extensions

Orchestra-Research/AI-Research-SKILLs

Extends transformer models’ context windows using RoPE, YaRN, ALiBi, and interpolation so LLMs can process documents of 32k–128k+ tokens, extrapolate to longer lengths, and deploy efficient positional encodings and bias strategies for fine-tuning or inference.

Mamba Selective State Models

mamba-architecture

Orchestra-Research/AI-Research-SKILLs

Mamba provides selective state-space models with O(n) inference complexity, letting you handle million-token sequences faster than transformers while skipping KV caches and benefiting from a hardware-aware design. Use it for long-context language modeling, streaming applications, and scalable low-memory sequence learners.

Minimalist GPT Training

Orchestra-Research/AI-Research-SKILLs

NanoGPT is a hackable ~300-line PyTorch reproduction of GPT-2 for learning transformer internals; train Shakespeare or OpenWebText on CPUs or multi-GPU setups, experiment with fine-tuning/custom datasets, and prototype quickly.

Clinical AI SDK Patterns

openevidence-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

Guides building clinical AI integrations with the OpenEvidence SDK, covering singleton client setup, typed query builders, response transformers, and caching strategies for reliable decision support.

Flash Attention Optimization

optimizing-attention-flash

Orchestra-Research/AI-Research-SKILLs

Speeds up transformer attention with Flash Attention techniques, offering 2-4× throughput gains and 10-20× memory reduction. Ideal for long-context models on PyTorch (native or flash-attn lib), H100 FP8, and sliding window scenarios to resolve GPU memory bottlenecks and improve inference.

Structured Text Generation

Orchestra-Research/AI-Research-SKILLs

Outlines guarantees valid JSON/XML/code generation via CFG-driven FSM filtering, Pydantic schemas, and fast local or API models (Transformers, vLLM, llama.cpp), making structured inference safe and high-performance.

LLM Quantization Toolkit

quantizing-models-bitsandbytes

Orchestra-Research/AI-Research-SKILLs

Quantizes LLMs to 8-bit or 4-bit to cut memory by 50-75%, letting inference and QLoRA fine-tuning run on constrained GPUs while keeping accuracy high; supports Bitsandbytes, NF4/INT8 formats, 8-bit optimizers, and HuggingFace Transformers.

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