Skill.md files: teach Claude once, not every chat.

• SKILL.md

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name: hypothesis-formulation
description: Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
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• SKILL.md

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

• SKILL.md

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name: researchclaw
description: Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
---

• SKILL.md

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name: scientific-visualization
description: Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
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• SKILL.md

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name: scientific-writing
description: Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
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• SKILL.md

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name: statistical-reporting
description: Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
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• SKILL.md

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name: fba-simulator
description: 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.
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• SKILL.md

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name: flux-analyzer
description: 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.
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• SKILL.md

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name: gsmm-builder
description: 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.
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• SKILL.md

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name: gsmm-validator
description: 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.
---

• SKILL.md

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name: metabolic-study-planner
description: 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 before FBA code is generated.
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• SKILL.md

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name: mfa-pipeline-orchestrator
description: 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.
---

• SKILL.md

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name: stat-research-orchestrator
description: Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
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• SKILL.md

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name: stat-result-validator
description: Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
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• SKILL.md

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name: statistical-experimental-evaluation
description: Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
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• SKILL.md

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name: statistical-method-design
description: Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
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• SKILL.md

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name: statistical-problem-formulation
description: Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.
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• SKILL.md

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name: statistical-theory-analysis
description: Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
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• SKILL.md

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name: cv-classification
description: Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
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• SKILL.md

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name: cv-detection
description: Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
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• SKILL.md

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name: nlp-alignment
description: Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
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• SKILL.md

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name: nlp-pretraining
description: Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
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• SKILL.md

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name: quantum-qiskit
description: 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 that imports `qiskit`, `qiskit_aer`, `qiskit_algorithms`, `qiskit_machine_learning`, or `qiskit_nature`.
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• SKILL.md

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