Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsMethodology Designer
Design a rigorous, reproducible methodology, with baselines, metrics, and ablations reviewers cannot reject.
$8
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
| Component | Choice | Justification |
|---|---|---|
| Dataset | HAM10000 + ISIC 2019 | Public, multi-class, standard benchmark |
| Split | Stratified 70/15/15 | Preserves rare-class proportions |
| Preprocessing | 224×224, color constancy | Matches pretrained backbones |
| Baselines | ResNet-50, EfficientNet-B4 | Strong CNN controls |
| Proposed | ViT-B/16 (ImageNet-21k pretrained) | Tests transformer claim |
| Metrics | Balanced accuracy, macro-F1, AUC | Handles class imbalance |
| Statistical test | McNemar + 5×2 cross-validation | Justifies significance |
Ablation plan
- Pretraining source (ImageNet vs. self-supervised DINO).
- Input resolution (224 vs. 384).
- 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-designerto turn the method into a methodology or pipeline figure. - Run
paper-outlineto map these into an IMRaD structure. - Run
reviewerto stress-test the design before running experiments.
methodology-designer.tsx
TSX · React component
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
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.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean 13 days ago
- Passed all security checks, Safe to install