aiming-lab/AutoResearchClaw

Agent skills in the GitHub repository aiming-lab/AutoResearchClaw, each with its code score out of 100.

Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞

Repository score

65 / 100 C

Skills

34

Verified

0

Average skill score

58

GitHub stars

14,862

License

MIT

Open aiming-lab/AutoResearchClaw on GitHub

34 skills in this repository

1. a-evolve

   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…

   75B
2. scientific-writing

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

   74B
3. scientific-visualization

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

   69C
4. flux-analyzer

   Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product predi…

   66C
5. gsmm-validator

   Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule…

   66C
6. researchclaw

   Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.

   66C
7. metabolic-study-planner

   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 hypo…

   65C
8. statistical-reporting

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

   65C
9. data-loading

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

   63C
10. biology-biopython

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

    62C
11. chemistry-rdkit

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

    62C
12. fba-simulator

    Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA…

    62C
13. gsmm-builder

    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 va…

    61C
14. quantum-qiskit

    Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigen…

    61C
15. distributed-training

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

    58C
16. experimental-design

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

    58C
17. pytorch-training

    Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

    58C
18. systematic-review

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

    58C
19. hypothesis-formulation

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

    56C
20. meta-analysis

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

    56C
21. mfa-pipeline-orchestrator

    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 mo…

    56C
22. mixed-precision

    Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

    55C
23. stat-result-validator

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

    54D
24. literature-search

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

    53D
25. stat-research-orchestrator

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

    52D
26. cv-classification

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

    51D
27. cv-detection

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

    51D
28. nlp-alignment

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

    51D
29. nlp-pretraining

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

    51D
30. rl-policy-optimization

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

    51D
31. statistical-experimental-evaluation

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

    50D
32. statistical-method-design

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

    50D
33. statistical-theory-analysis

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

    50D
34. statistical-problem-formulation

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

    49D

Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.