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    Methodology Designer

    1

    Design a rigorous, reproducible methodology, with baselines, metrics, and ablations reviewers cannot reject.

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    methodology-designer

    methodology-designer

    Example session with this skill installed

    Use methodology-designer to design an experimental methodology for comparing three deep learning models on skin-lesion classification.

    • Read your context and instructions
    • Compiled the methodology-designer
    • Generated the UI component

    Designed a reproducible methodology for a three-model comparison (ResNet-50, EfficientNet-B4, ViT-B/16) on skin-lesion classification, with explicit baselines, metrics, ablations, and a threat-to-validity plan.

    Problem definition

    Multi-class classification of dermoscopic images into 7 lesion categories (HAM10000 classes). Research question: does a vision transformer outperform CNNs under class imbalance and limited labeled data?

    Experimental protocol

    ComponentChoiceJustification
    DatasetHAM10000 + ISIC 2019Public, multi-class, standard benchmark
    SplitStratified 70/15/15Preserves rare-class proportions
    Preprocessing224×224, color constancyMatches pretrained backbones
    BaselinesResNet-50, EfficientNet-B4Strong CNN controls
    ProposedViT-B/16 (ImageNet-21k pretrained)Tests transformer claim
    MetricsBalanced accuracy, macro-F1, AUCHandles class imbalance
    Statistical testMcNemar + 5×2 cross-validationJustifies significance

    Ablation plan

    1. Pretraining source (ImageNet vs. self-supervised DINO).
    2. Input resolution (224 vs. 384).
    3. Data augmentation (none, light, heavy).

    Reproducibility

    • Seed all randomness; release configs, optimizer settings, and the augmentation pipeline.
    • Report hardware and training time; use a fixed environment (Docker).

    Next steps

    • Run figure-designer to turn the method into a methodology or pipeline figure.
    • Run paper-outline to map these into an IMRaD structure.
    • Run reviewer to stress-test the design before running experiments.

    methodology-designer.tsx

    TSX · React component

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Formalize problem definitions with precise mathematical notation.Design ablation studies to isolate specific technical contributions.Justify design choices against specific SOTA alternatives.Generate reproducibility checklists for conference submissions.

    About this skill

    The problem

    A weak methodology is the number one reason reviewers reject technically strong work. Researchers often describe what they did without justifying why each design choice was made, which leaves reviewers unable to trust the results. This skill designs a rigorous, reproducible methodology that stands up to scrutiny.

    What it does

    • Structures the method description and formalizes the problem definition.
    • Designs the experimental protocol and selects appropriate baselines and metrics.
    • Justifies every design choice with the alternatives considered and the expected effect.
    • Plans ablation studies, sensitivity analysis, and statistical testing.
    • Produces a reproducibility checklist with enough implementation detail to rerun the work.

    Why this beats prompting it yourself

    Generic prompts return a vague methods section that describes the approach but never justifies it. This skill follows a five-component framework, covering problem formalization, architecture, design justification, experimental protocol, and reproducibility, so every choice is tied to evidence and every experiment answers a specific research question.

    Use cases

    • Writing the methodology section of a paper or PhD proposal.
    • Designing experiments before running them, instead of reverse-engineering them afterward.
    • Selecting honest baselines and metrics that reviewers cannot dismiss.
    • Planning ablation studies that prove which components actually matter.
    • Reviewing an existing methodology for weaknesses before submission.

    Known limitations

    The design is only as good as the information you provide; it needs specific inputs about the problem scope, the proposed approach, the key innovation, and the available resources to be effective. It does not perform actual code execution or statistical computation, and it does not run the experiments; it produces a specification, so following the reproducibility checklist is still your responsibility.

    Important

    It designs the methodology and experimental protocol; it does not run the experiments or produce results for you. Pair it with paper-outline to map the design into a manuscript structure.

    Why buy it

    Use it when writing a methodology section, designing experiments, or planning a project's technical approach. It turns a fragile, hand-wavy methods section into a rigorous, reproducible plan.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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      Ask your agent to use it

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