AutoResearchClaw

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.

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

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

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

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

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`.

Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.

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

Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.

Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.

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

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

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

Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.

Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.

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

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

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