Browse Skills

Master XGBoost for tabular data problems including hyperparameter optimization, feature engineering, SHAP-based interpretability, and production deployment with drift monitoring.

Build production-grade AI-powered features using the Vercel AI SDK with streaming responses, structured outputs, tool calling, and multi-provider support in Next.js applications.

Expert guidance on designing, building, and querying knowledge graphs using ontology engineering, graph databases, NLP-based construction pipelines, and LLM/GraphRAG integration patterns.

Designs, builds, and validates high-quality training datasets for LoRA fine-tuning of large language models, applying data-centric AI principles and instruction-tuning best practices to maximize model performance per training example.

Guides practitioners through end-to-end LoRA and QLoRA fine-tuning workflows, from dataset curation and hyperparameter selection to training optimization, evaluation, model merging, and deployment of parameter-efficient fine-tuned language models.

Design and implement machine learning-based lead scoring systems that predict conversion probability, prioritize sales efforts, and automate lead qualification using behavioral, firmographic, and engagement data from CRM platforms.

Design and deploy predictive maintenance systems using edge computing, IoT sensor data, and machine learning models for anomaly detection and remaining useful life estimation in industrial environments.

Expert guidance for building production-grade LLM applications with LangChain. Covers chain composition with LCEL, RAG pipelines, agent systems, LangGraph workflows, prompt engineering, and production deployment with LangSmith observability.

Expert in TensorFlow and Keras — model building, training, TF Serving, TF Lite, TFX pipelines, and production deployment on Google Cloud.

Provides expert guidance on transformer neural network architectures, including attention mechanisms, positional encoding schemes, encoder-decoder design patterns, tokenization strategies, and context window optimization for building and understanding modern LLMs.

Design safe, effective tool-using AI agents with proper tool schemas, agent loop architecture, safety gates, and evaluation strategies for production LLM applications.

Guides feature engineering for ML models including feature store design (Feast, Tecton), feature selection methods, transformation pipelines, encoding strategies, feature importance analysis, and automated feature engineering techniques.

Design and optimize production-grade NLP pipelines covering text preprocessing, tokenization, NER, sentiment analysis, and transformer-based architectures using spaCy and HuggingFace.

Expert in AI safety evaluation — adversarial testing, jailbreak prevention, prompt injection defense, bias detection, and safety benchmarking for LLMs and AI systems.

Design comprehensive evaluation systems for LLMs and AI applications, covering automated metrics, human evaluation protocols, LLM-as-a-judge pipelines, and CI/CD-integrated regression testing.

Design and implement MLOps platforms covering model serving, feature stores, CI/CD for ML, model monitoring, drift detection, A/B testing, and GPU resource management.

Reduces LLM API costs through systematic token optimization — prompt compression, caching strategies, model routing, and batch processing — while maintaining output quality across OpenAI, Anthropic, and open-source models.

Designs, builds, and optimizes complex multi-step Zapier workflows connecting multiple SaaS applications, with branching logic, error handling, sub-Zaps, and task optimization for production-grade business automation.

Designs multi-stage ML model deployment pipelines with automated rollback triggers, canary releases, and shadow deployments to ensure safe, reversible model updates in production.

Designs optimal text chunking strategies for Retrieval-Augmented Generation (RAG) systems, balancing chunk size, semantic coherence, retrieval precision, and generation quality across diverse document types.

Design and build reliable API integrations in Make (formerly Integromat) using HTTP modules, OAuth flows, pagination handling, and advanced error recovery patterns.

Guides the design of reinforcement learning systems including policy selection (PPO, SAC, DQN), environment design, reward shaping, exploration strategies, multi-agent configurations, and sim-to-real transfer for robotics and game AI.

Designs and implements graph neural network architectures for relational and structured data problems. Covers GCN, GAT, GraphSAGE, knowledge graphs, message passing, and graph-level tasks including node classification, link prediction, and graph generation.

Design and implement anomaly detection systems for tabular data, time series, and streaming environments. Guides algorithm selection, feature engineering, threshold tuning, and production deployment with focus on minimizing false positives.