aiming-lab/AutoResearchClaw

Diagnoses where an agent failed across runs and turns the findings into new skills, system prompt patches and knowledge entries, using the A-Evolve loop.

aiming-lab/AutoResearchClaw

Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

aiming-lab/AutoResearchClaw

Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

aiming-lab/AutoResearchClaw

Reference patterns for writing qiskit 2.x code for variational quantum machine learning: feature maps, VQC training, VQE for chemistry, MPS circuits and noise models.

aiming-lab/AutoResearchClaw

Builds or loads a genome-scale metabolic model in COBRApy, sets its growth medium and objective, and exports it as a validated JSON file for flux analysis.

aiming-lab/AutoResearchClaw

Quick reference for Biopython work: sequence operations, SeqIO file parsing, BLAST searches, Entrez queries, phylogenetic trees and PDB structure analysis.

aiming-lab/AutoResearchClaw

Reference guide for working with molecules in RDKit: reading SMILES and SDF files, computing descriptors and fingerprints, and searching substructures.

aiming-lab/AutoResearchClaw

Runs flux balance analysis and related constraint-based simulations on a COBRApy metabolic model, from standard FBA to gene knockouts and carbon source swaps.

aiming-lab/AutoResearchClaw

Turns raw flux balance analysis output and a COBRApy model into gene essentiality maps, phenotypic phase planes, flux sampling results, pathway summaries and secretion predictions.

aiming-lab/AutoResearchClaw

Runs quality control on a COBRApy genome-scale metabolic model before flux analysis, checking mass and charge balance, biomass feasibility, dead ends, thermodynamic loops and GPR rules.

aiming-lab/AutoResearchClaw

Guides turning an observation into testable, falsifiable hypotheses with null and alternative statements, competing explanations and predictions tied to experimental design.

aiming-lab/AutoResearchClaw

Lays out a systematic literature review method: PICO-based search strategy, inclusion criteria, PRISMA screening, quality assessment tools and synthesis approaches.

aiming-lab/AutoResearchClaw

Runs the ResearchClaw 23-stage autonomous pipeline from a topic, config file and output directory, from literature review through experiments to a written and reviewed paper.

aiming-lab/AutoResearchClaw

Publication-ready scientific figure design with matplotlib and seaborn.

aiming-lab/AutoResearchClaw

Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines.

aiming-lab/AutoResearchClaw

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

aiming-lab/AutoResearchClaw

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim…

aiming-lab/AutoResearchClaw

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

aiming-lab/AutoResearchClaw

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

aiming-lab/AutoResearchClaw

Statistical test selection, assumption checking, and APA-formatted reporting.

aiming-lab/AutoResearchClaw

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

aiming-lab/AutoResearchClaw

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

aiming-lab/AutoResearchClaw

Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning.

aiming-lab/AutoResearchClaw

Best practices for language model pretraining and fine-tuning.

aiming-lab/AutoResearchClaw

Best practices for reinforcement learning policy optimization.

aiming-lab/AutoResearchClaw

Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Optimize data loading pipeline to prevent GPU starvation. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Multi-GPU and distributed training patterns with PyTorch DDP.

aiming-lab/AutoResearchClaw

Statistical methods for combining results across multiple studies.

aiming-lab/AutoResearchClaw

Use FP16/BF16 mixed precision to accelerate training and reduce memory.

aiming-lab/AutoResearchClaw

Best practices for building robust PyTorch training loops. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Structured methodology for comprehensive literature review following PRISMA guidelines.

aiming-lab/AutoResearchClaw