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abelrguezr
GitHub 创作者资料

abelrguezr

按仓库查看 1 个 GitHub 仓库中的 908 个已收集 skills。

已收集 skills
908
仓库
1
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2026年3月23日
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仓库与代表性 skills

ai-fuzzing-assistant
信息安全分析师

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…

2026年3月23日
burp-mcp-integration
信息安全分析师

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…

2026年3月23日
deep-learning-helper
数据科学家

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…

2026年3月23日
llm-fundamentals
高校计算机科学教师

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…

2026年3月23日
text-tokenizer
数据科学家

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…

2026年3月23日
llm-data-sampling
数据科学家

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…

2026年3月23日
token-embeddings
软件开发工程师

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…

2026年3月23日
attention-mechanisms
数据科学家

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…

2026年3月23日
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