AutoResearchClaw Skill

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This set has 34 skills, too many to install at once: every installed skill’s description loads into every chat, so the AI picks the wrong one more easily. Pick only the ones you use from the list below

Showing 28 of 34 See all on GitHub

• 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 patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipeline…

• biology-biopython

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

• chemistry-rdkit

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

• cv-classification

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

• cv-detection

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

• experimental-design

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

• 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, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

• flux-analyzer

  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.

• 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 validated .json model files.

• 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 formatting. Outputs a structured validation report with errors and warnings.

• hypothesis-formulation

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

• literature-search

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

• 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 hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls bef…

• 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 model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.

• nlp-alignment

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

• nlp-pretraining

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

• quantum-qiskit

  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 integration. Use when writing Python code tha…

• researchclaw

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

• rl-policy-optimization

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

• scientific-visualization

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

• scientific-writing

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

• stat-research-orchestrator

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

• stat-result-validator

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

• statistical-experimental-evaluation

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

• statistical-method-design

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

• statistical-problem-formulation

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

• statistical-reporting

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

• statistical-theory-analysis

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