AutoResearchClaw

Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.

Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.

Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.

Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigensolver (VQE) for chemistry, matrix-product-state circuits, and noise model integration. Use when writing Python code that imports `qiskit`, `qiskit_aer`, `qiskit_algorithms`, `qiskit_machine_learning`, or `qiskit_nature`.

Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.

Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.

Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.

Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.