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testing security llm ai AI Skills

Discover testing security llm ai skills, compare source repositories and read the instructions for each workflow.

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GitHub

quantizing-models-bitsandbytes

davila7

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLo...

☆ 18,948View skill
GitHub

pufferlib

davila7

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integrati...

☆ 18,948View skill
GitHub

phoenix-observability

davila7

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems...

☆ 18,948View skill
GitHub

sparse-autoencoder-training

davila7

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing...

☆ 18,947View skill
GitHub

llama-factory

davila7

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

☆ 18,947View skill
GitHub

ux-researcher-designer

davila7

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona c...

☆ 18,947View skill
GitHub

feature-design-assistant

davila7

Turn ideas into fully formed designs and specs through natural collaborative dialogue. Use when planning new features, designing architecture, or making significant changes to the codebase.

☆ 18,947View skill
GitHub

denario

davila7

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 re...

☆ 18,947View skill
GitHub

nanogpt

davila7

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch....

☆ 18,947View skill
GitHub

n8n-workflow-patterns

davila7

Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook...

☆ 18,947View skill
GitHub

evaluating-llms-harness

davila7

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. I...

☆ 18,947View skill
GitHub

stable-diffusion-image-generation

davila7

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building cus...

☆ 18,947View skill
GitHub

skill-creation-guide

davila7

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or to...

☆ 18,947View skill
GitHub

peft-fine-tuning

davila7

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train

☆ 18,947View skill
GitHub

moe-training

davila7

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtra...

☆ 18,945View skill
GitHub

fine-tuning-with-trl

davila7

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with...

☆ 18,945View skill
GitHub

langsmith-observability

davila7

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing...

☆ 18,945View skill
GitHub

senior-prompt-engineer

davila7

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-though...

☆ 18,945View skill
GitHub

instructor

davila7

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested struc...

☆ 18,945View skill
GitHub

blip-2-vision-language

davila7

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero...

☆ 18,945View skill
GitHub

dspy

davila7

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

☆ 18,945View skill
GitHub

unsloth

davila7

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

☆ 18,943View skill
GitHub

hypogenic

davila7

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses...

☆ 18,943View skill

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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