davila7/claude-code-templates

Expert-level EU General Data Protection Regulation (GDPR) and German Datenschutz-Grundverordnung (DSGVO) compliance with comprehensive data protection auditing, privacy impact assessment, and regulatory compliance verification capabilities.

Comprehensive assistance with pytorch-fsdp development, generated from official documentation.

Source details, install context, and public review data are available on the full page.

Accelerate simplifies distributed training to 4 lines of code.

vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block-based KV cache) and continuous batching (mixing prefill/decode requests).

The AI-native database for building LLM applications with memory.

Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.

Comprehensive assistance with deepspeed development, generated from official documentation.

This skill enables the transformation of academic papers into multiple promotional and presentation formats using the Paper2All autonomous pipeline. The system converts research papers (LaTeX or PDF) into three primary outputs:

Python framework for sentence and text embeddings using transformers.

Source details, install context, and public review data are available on the full page.

lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.

Execute implementation tasks directly from design documents. Tasks are managed as markdown checkboxes - no separate session files needed.

Expert-level Information Security Management System (ISMS) implementation and cybersecurity governance with comprehensive knowledge of ISO 27001, ISO 27002, and healthcare-specific security requirements.

Development platform for debugging, evaluating, and monitoring language models and AI applications.

Implements the NOWAIT technique from the paper "Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency" (Wang et al., 2025).

Source details, install context, and public review data are available on the full page.

Source details, install context, and public review data are available on the full page.

Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses.

Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.

The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.

Source details, install context, and public review data are available on the full page.

Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.

Megatron-Core trains LLMs from 2B to 462B parameters with up to 47% Model FLOP Utilization on H100 GPUs through advanced parallelism strategies.