Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞
AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
This repository also configures its own agents. See what AutoResearchClaw tells them →
Skill Claude CodeCodex
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.
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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.
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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.
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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.
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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…
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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.
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Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
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Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
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Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
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Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
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Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.
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Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
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Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
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Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
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Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
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Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
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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…
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Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
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Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
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Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
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Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
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Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
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Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
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Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
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