Design Pack
From Idea to a Structured Publication Plan
Works with every agent that reads SKILL.md — Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Windsurf, OpenClaw, and more.
One payment, lifetime access. 4 skills unlock instantly in your library.
30-day refund guarantee
Instant unlock in your library
Free updates from the creator
What's included
4 skills
Design a rigorous, reproducible methodology, with baselines, metrics, and ablations reviewers cannot reject.
Turn any methodology, pipeline, or result into a publication-ready figure, with spec, caption, and renderable code.
Get a full IMRaD (introduction, method, results and discussion) outline with page budgets, key messages, and figure placement before you write a single word.
Generate venue-ready paper titles in multiple styles, scored for impact, readability, and discoverability.
See it in action
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.
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 skill in this bundle passes our trust checks.
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