What problem does it solve?
This Skill solves the common research pain point of scattered, incoherent formula notes and unclear derivation logic that cannot be directly used for academic paper theory sections, saving researchers significant time reorganizing and refining their theoretical work.
Core Features & Use Cases
- Coherent Derivation Packaging: Organizes scattered formulas, messy notes, and informal theory sketches into structured, logically consistent derivation packages.
- Assumption & Notation Normalization: Identifies and standardizes ambiguous assumptions, undefined notation, and hidden scope boundaries to ensure derivation consistency.
- Step Classification & Honest Labeling: Correctly classifies each derivation step as identity, proposition, approximation, or interpretation to avoid overclaiming and maintain academic rigor.
- Use Case: A researcher working on the FMC-Net vertebrae segmentation model has messy notes on the wavelet downsampling loss function with scattered equations and unclear assumptions; this Skill can turn those notes into a structured, paper-ready derivation document with explicit step labels and stated boundaries.
Quick Start
Use the formula-derivation skill to organize my scattered notes on the high-frequency feature refinement module's mathematical formulation into a coherent derivation package for my MICCAI 2025 paper submission.
