3. Data, Science & AI

Provides techniques to craft effective prompts for LLMs, optimizing model outputs and agent instructions.

Matches observed 13C NMR shifts against reference databases to identify known compounds, enabling quick dereplication before full structural analysis.

Executes parallel AI model reviews on specification documents and outputs structured Council artifacts for collaborative decision-making.

Computer-assisted structure elucidation for organic natural products using NMR data, including spectrum analysis and database dereplication.

Performs full de novo chemical structure elucidation from NMR correlations, bypassing dereplication for novel compounds or first-principles analysis.

Verifies translations of manual pages against corresponding images using AI, identifies extraction failures, and regenerates text from images.

Manages Kaggle API authentication, dataset operations, and notebook execution for data science competitions on Texas Tech HPCC, including VPN checks.

Automates PDF-to-structured-data conversion for Next.js applications using split, extract, translate, and build steps.

Guides users in selecting the optimal AI model (Flash, Sonnet, Pro) based on task requirements for efficient AI deployment.

Enables text search, document similarity matching, and relevance ranking using TF-IDF vector space models and cosine similarity for text datasets.

Trains LangChain and LangGraph agents using Microsoft Agent-Lightning APO for prompt optimization in single and multi-agent systems.

Configures DuckDB databases for data processing, including initialization and settings setup.

Processes multiple data variants for classification, providing real-time progress tracking and summary reports.

Performs ACMG/AMP-compliant classification of genetic variants by validating input, gathering evidence, and applying standardized rules to generate clinical classifications.

Provides AI-powered customer service with chatbot, recommendation engine, and NLP for user intent understanding on real_deal platform.

Enables training and deployment of neural networks in distributed E2B sandboxes using Flow Nexus platform.

Optimizes AgentDB performance via quantization, HNSW indexing, caching, and batch operations to reduce memory usage and accelerate vector search at scale.

Enables advanced vector search, multi-database management, and distributed system integration for AI applications and multi-agent coordination.

Enables adaptive learning systems using AgentDB's vector database for self-learning agents, featuring trajectory tracking and memory distillation to optimize decision-making.

Enables adaptive learning systems using ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in self-learning agents and meta-cognitive systems.

Orchestrates multi-agent swarms for parallel task execution, dynamic topology, and intelligent coordination in distributed AI systems.

Manages RAG system operations including knowledge ingestion, vector search, embedding management, and retrieval debugging.

Enables persistent memory patterns for AI agents using AgentDB, supporting session memory, long-term storage, and context management in stateful applications.

Enables efficient data analytics with DuckDB, MotherDuck cloud integration, and Parquet file handling in Node.js/TypeScript environments.