AI-assisted fuzzing and vulnerability discovery. Use this skill whenever the user wants to generate fuzzing seeds, evolve grammars, analyze crashes, create proof-of-vulnerability exploits, or generate patches for discovered bugs. Trigger on mentions of fuzzing, AFL++, libFuzzer, vulnerability discovery, crash analysis, exploit generation, or security testing with LLMs.
Set up and use Burp Suite's MCP Server extension to enable LLM-assisted passive vulnerability discovery. Use this skill whenever the user wants to integrate Burp with MCP-capable AI tools (Codex, Gemini, Ollama, Claude), configure the MCP proxy, troubleshoot handshake issues, or analyze intercepted HTTP traffic for security findings. Trigger on mentions of Burp MCP, Burp AI Agent, MCP proxy setup, or LLM-assisted traffic review.
Help users understand and implement deep learning concepts including neural networks, CNNs, RNNs, LLMs, and diffusion models. Use this skill whenever the user asks about deep learning architectures, wants to build neural networks in PyTorch, needs help with training loops, or wants to understand concepts like backpropagation, activation functions, attention mechanisms, or generative models. Make sure to use this skill for any deep learning related questions, code reviews, architecture design, or implementation help.
Explain and teach Large Language Model fundamentals including pretraining, model architecture, PyTorch tensors, automatic differentiation, and backpropagation. Use this skill whenever the user asks about LLM concepts, neural network training, PyTorch operations, gradient computation, or wants to understand how LLMs work internally. Trigger on questions about model parameters, context length, embedding dimensions, tensor operations, autograd, or backpropagation.
How to tokenize text for LLMs and NLP models. Use this skill whenever the user needs to convert text into token IDs, understand tokenization methods (BPE, WordPiece, Unigram), work with vocabularies, or implement tokenization for machine learning. Make sure to use this skill when users mention tokenizing, token IDs, vocabulary creation, BPE, WordPiece, or any text preprocessing for ML models.
How to prepare and sample text data for training large language models. Use this skill whenever the user mentions data preparation, tokenization, sliding windows, sequence generation, training data, LLM datasets, or needs to create input/target pairs for model training. This includes tasks like chunking text, creating dataloaders, applying sampling strategies, or optimizing training data quality.
Create and work with token embeddings for LLMs. Use this skill whenever you need to understand token embeddings, create embedding layers in PyTorch, add positional embeddings (absolute, relative, or RoPE), or debug embedding-related issues in your language model. This skill covers vocabulary setup, embedding initialization, positional encoding strategies, and context window extension techniques. Make sure to use this skill when working with any LLM architecture, training pipelines, or when you need to convert tokens to numerical vectors.
How to implement and understand attention mechanisms in neural networks and LLMs. Use this skill whenever the user needs to build self-attention layers, causal attention, multi-head attention, or understand how attention weights are calculated. Trigger this skill for any task involving attention scores, Q/K/V matrices, attention masking, or transformer architecture components.