القائمة
جمع SkillsMP عدد ٣٤ من skills من aiming-lab/AutoResearchClaw. افتح أي skill لمراجعة مصدره وتفاصيله.
عرض ٣٤ من أصل ٣٤ skills مجمعة.
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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,…
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
لغة النص الأصلي: الإنجليزية
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
لغة النص الأصلي: الإنجليزية
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
لغة النص الأصلي: الإنجليزية
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
لغة النص الأصلي: الإنجليزية
Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.
لغة النص الأصلي: الإنجليزية
Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
لغة النص الأصلي: الإنجليزية
Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
لغة النص الأصلي: الإنجليزية
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
لغة النص الأصلي: الإنجليزية
Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
لغة النص الأصلي: الإنجليزية
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
لغة النص الأصلي: الإنجليزية
Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
لغة النص الأصلي: الإنجليزية
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
لغة النص الأصلي: الإنجليزية
Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
لغة النص الأصلي: الإنجليزية
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
لغة النص الأصلي: الإنجليزية
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
لغة النص الأصلي: الإنجليزية
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
لغة النص الأصلي: الإنجليزية
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
لغة النص الأصلي: الإنجليزية
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
لغة النص الأصلي: الإنجليزية
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
لغة النص الأصلي: الإنجليزية
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
لغة النص الأصلي: الإنجليزية
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
لغة النص الأصلي: الإنجليزية
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
لغة النص الأصلي: الإنجليزية
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
لغة النص الأصلي: الإنجليزية
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
لغة النص الأصلي: الإنجليزية