3. Data, Science & AI

Analyzes completed PRs with agent involvement to extract behavioral lessons, identify patterns, and generate actionable recommendations for improving agent skills and documentation.

Enables development of machine learning and AI applications in Rust, supporting model training, inference, and deep learning with Rust libraries.

Creates, alters, and validates Snowflake semantic views using Snowflake CLI, including DDL validation and setup guidance.

Merges fine-tuned AI models using techniques like SLERP and Task Arithmetic to combine domain expertise without retraining, enhancing performance and enabling rapid experimentation.

Provides SHAP-based model interpretability for explaining predictions, feature importance, and bias analysis across ML models.

Comprehensive statistical analysis toolkit for academic research, including hypothesis testing, regression, Bayesian methods, and APA reporting.

Queries PubChem database for chemical compounds, supporting searches by name, CID, SMILES, and retrieving properties, bioactivity, and similarity data.

Autonomous AI framework for biomedical research, executing complex tasks in genomics, drug discovery, and clinical analysis using LLM reasoning and integrated databases.

Comprehensive toolkit for protein language models (ESM3, ESM C) enabling sequence, structure, and function prediction, design, and engineering tasks.

Enables 4/3/2-bit quantization of LLMs without calibration data, accelerating deployment via vLLM and HuggingFace Transformers.

Provides a reference-free, efficient alternative to DPO for LLM preference alignment, achieving better performance with simpler training.

PyTorch-based toolkit for biomedical graph machine learning, featuring GNNs for molecular property prediction, protein modeling, and drug discovery tasks.

Direct REST API access to KEGG database for academic research, enabling pathway analysis, gene mapping, and metabolic pathway exploration.

A Python library for machine learning tasks including classification, regression, clustering, and model evaluation.

Accesses Human Metabolome Database for searching metabolites, retrieving chemical properties, spectra, and pathways to support metabolomics research.

Evaluates code generation models across HumanEval, MBPP, and MultiPL-E benchmarks using pass@k metrics for quality assessment.

Provides a fully managed vector database for production AI applications, supporting RAG, semantic search, and scalable recommendation systems with low latency.

Accelerates LLM inference with RadixAttention prefix caching for structured JSON/regex outputs, constrained decoding, and agentic workflows.

High-performance data manipulation library using Apache Arrow for efficient filtering, grouping, and I/O operations in data analysis workflows.

High-performance RLHF framework for training large language models (7B-70B+) using Ray, vLLM, and ZeRO-3, supporting PPO, DPO, and distributed training.

Provides activation-aware weight quantization for 4-bit LLM compression, enabling 3x speedup with minimal accuracy loss on limited GPU memory.

Provides an RNN-Transformer hybrid architecture for efficient AI inference with linear time complexity, infinite context, and production-ready large language models.

Comprehensive Python toolkit for creating, analyzing, and visualizing complex networks and graphs with graph algorithms and community detection capabilities.

Provides constitutional AI training for aligning language models with human values using self-critique and AI feedback to reduce harmful outputs without human labels.