Browse Skills

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.

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

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.

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.

Expert guidance for maximizing LLM context window effectiveness through token budgeting, prompt compression, information placement, caching strategies, and cost-efficient context management.

Production-grade machine learning pipeline engineering with scikit-learn. Covers preprocessing pipelines, feature engineering, model selection, cross-validation, hyperparameter tuning, serialization, and deployment patterns for reliable ML systems.

Design effective chain-of-thought prompting strategies that elicit step-by-step reasoning from large language models for complex problem-solving, analysis, and decision-making tasks.

Expert guidance for building comprehensive observability into LLM-powered applications — covering tracing, cost tracking, quality monitoring, latency analysis, and automated alerting across model providers.

Guide the end-to-end curation of high-quality datasets for machine learning. Covers data collection strategies, cleaning pipelines, annotation workflows, bias auditing, deduplication, and dataset documentation following data-centric AI principles.

Expert architect for designing multi-step prompt chains and orchestration patterns — from task decomposition and chain-of-thought sequencing to intermediate verification gates, context window management, error recovery strategies, and production-grade prompt pipeline design for complex LLM workflows.

Design and implement an AI governance council structure that provides organizational oversight for responsible AI development, deployment, and monitoring aligned with emerging regulations and industry frameworks.

Builds structured competitive intelligence analyses including competitor profiling, feature parity matrices, win/loss patterns, pricing intelligence, and sales battlecards using proven CI frameworks and market research methodologies.

Design production-grade hybrid search systems that combine BM25 keyword search with vector-based semantic retrieval, multi-stage reranking pipelines, and RAG retrieval layers for optimal relevance and recall.

Guides decisions on when and how to fine-tune large language models, covering LoRA/QLoRA, dataset curation, RLHF/DPO alignment, evaluation strategies, and cost-performance tradeoffs for production deployments.

Performance tuning for Pinecone, Weaviate, Qdrant, and pgvector including index selection, embedding dimensionality, chunking strategies, and hybrid search optimization for RAG systems.

Designs production-grade Retrieval-Augmented Generation pipelines including chunking strategies, embedding model selection, vector database architecture, hybrid search, reranking, and hallucination reduction techniques.

Design synthetic data generation pipelines that produce privacy-preserving, statistically faithful datasets for ML training, testing, and data sharing using GANs, copulas, and differential privacy.

Guides the end-to-end deployment of TensorFlow Lite models on mobile and edge devices — from model conversion and quantization to on-device inference optimization, hardware delegate selection, and production monitoring. Ensures models meet latency, size, and accuracy constraints for resource-constrained environments.

Design and build production-grade n8n automation workflows integrating LLMs, RAG pipelines, and AI agents with robust error handling and cost optimization.

Designs comprehensive data labeling strategies including annotation pipeline architecture, inter-annotator agreement measurement, active learning loops, label quality control, crowdsourcing management, few-shot labeling, and weak supervision techniques.

Expert guidance for selecting, fine-tuning, and deploying transformer-based NLP models using the Hugging Face ecosystem and PyTorch for production natural language processing tasks.