aiming-lab/AutoResearchClaw

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name: fba-simulator

description: 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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name: flux-analyzer

description: 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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name: gsmm-builder

description: 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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name: gsmm-validator

description: 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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name: metabolic-study-planner

description: 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.

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name: mfa-pipeline-orchestrator

description: 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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name: statistical-experimental-evaluation

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

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name: statistical-method-design

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

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name: statistical-problem-formulation

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

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name: statistical-theory-analysis

description: 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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name: stat-research-orchestrator

description: 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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name: stat-result-validator

description: 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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name: biology-biopython

description: Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.

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name: chemistry-rdkit

description: Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.

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name: cv-classification

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

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name: cv-detection

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

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name: nlp-alignment

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

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name: nlp-pretraining

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

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name: quantum-qiskit

description: 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`.

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name: rl-policy-optimization

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

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name: experimental-design

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

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name: hypothesis-formulation

description: Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.

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name: literature-search

description: Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.

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name: meta-analysis

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

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name: scientific-visualization

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

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name: scientific-writing

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

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name: statistical-reporting

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

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name: systematic-review

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

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name: data-loading

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

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name: distributed-training

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