Menu
O SkillsMP coletou 34 skills de aiming-lab/AutoResearchClaw. Abra uma skill para revisar a origem e os detalhes.
Mostrando 34 de 34 skills coletadas.
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…
Idioma do texto original: inglês
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.
Idioma do texto original: inglês
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.
Idioma do texto original: inglês
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.
Idioma do texto original: inglês
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,…
Idioma do texto original: inglês
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…
Idioma do texto original: inglês
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
Idioma do texto original: inglês
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
Idioma do texto original: inglês
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
Idioma do texto original: inglês
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
Idioma do texto original: inglês
Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.
Idioma do texto original: inglês
Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
Idioma do texto original: inglês
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…
Idioma do texto original: inglês
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
Idioma do texto original: inglês
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…
Idioma do texto original: inglês
Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
Idioma do texto original: inglês
Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
Idioma do texto original: inglês
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
Idioma do texto original: inglês
Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
Idioma do texto original: inglês
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
Idioma do texto original: inglês
Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
Idioma do texto original: inglês
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
Idioma do texto original: inglês
Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
Idioma do texto original: inglês
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Idioma do texto original: inglês
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
Idioma do texto original: inglês
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
Idioma do texto original: inglês
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
Idioma do texto original: inglês
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
Idioma do texto original: inglês
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
Idioma do texto original: inglês
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
Idioma do texto original: inglês
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
Idioma do texto original: inglês
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
Idioma do texto original: inglês
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
Idioma do texto original: inglês
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
Idioma do texto original: inglês