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Agent skills
generative-ai

Topic: generative-ai

187 skills in this topic.

finops-expert Expert-level cloud financial operations, cost optimization, and cloud economics
personamanagmentlayer/pcl 13
lawyer-expert Expert-level legal systems, contracts, compliance, and legal technology
personamanagmentlayer/pcl 13
standards-expert Expert-level ISO standards, quality management, compliance, and certification
personamanagmentlayer/pcl 13
biological-expert Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology
personamanagmentlayer/pcl 13
quantum-expert Expert-level quantum computing, Qiskit, quantum algorithms, and quantum information
personamanagmentlayer/pcl 13
research-expert Expert-level research methodology, academic writing, statistical analysis, and scientific investigation
personamanagmentlayer/pcl 13
code-review-expert Expert-level code review focusing on quality, security, performance, and maintainability. Use this skill for conducting thorough code reviews, identifying issues, and providing constructive feedback.
personamanagmentlayer/pcl 13
git-expert Expert-level Git version control with advanced workflows, branching strategies, and best practices for team collaboration
personamanagmentlayer/pcl 13
skill-creator-expert Expert system for designing, creating, and validating PCL skills with comprehensive domain knowledge extraction
personamanagmentlayer/pcl 13
testing-expert Expert-level software testing with unit tests, integration tests, E2E tests, TDD/BDD, and testing best practices
personamanagmentlayer/pcl 13
context-engineering Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
itsmostafa/llm-engineering-skills 17
lora Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
itsmostafa/llm-engineering-skills 17
mlx Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
itsmostafa/llm-engineering-skills 17
pytorch Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
itsmostafa/llm-engineering-skills 17
agents Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
itsmostafa/llm-engineering-skills 17
prompt-engineering Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.
itsmostafa/llm-engineering-skills 17
qlora Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
itsmostafa/llm-engineering-skills 17
rlhf Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
itsmostafa/llm-engineering-skills 17
transformers Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
itsmostafa/llm-engineering-skills 17