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SkillsMP は aiming-lab/AutoResearchClaw から 34 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 34 件中 34 件を表示しています。
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…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
原文の言語: 英語
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
原文の言語: 英語
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
原文の言語: 英語
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
原文の言語: 英語
Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.
原文の言語: 英語
Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
原文の言語: 英語
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…
原文の言語: 英語
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
原文の言語: 英語
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works…
原文の言語: 英語
Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
原文の言語: 英語
Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
原文の言語: 英語
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
原文の言語: 英語
Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
原文の言語: 英語
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
原文の言語: 英語
Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
原文の言語: 英語
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
原文の言語: 英語
Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
原文の言語: 英語
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
原文の言語: 英語
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
原文の言語: 英語
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
原文の言語: 英語
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
原文の言語: 英語
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
原文の言語: 英語
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
原文の言語: 英語
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
原文の言語: 英語
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
原文の言語: 英語
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
原文の言語: 英語
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
原文の言語: 英語
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
原文の言語: 英語