aiml-expert

What problem does it solve?

This Skill helps you write, evaluate, and choose AI/ML methods for scientific imaging tasks—especially cryo-EM—so your explanations are accurate, testable, and appropriately critical of benchmarks and claims.

Core Features & Use Cases

• Model-and-method translation: Converts ML concepts (architectures, losses, training paradigms) into imaging-task language readers can use.
• Evidence-based tool descriptions: Guides you to describe inputs/outputs, architecture family, training data, inference cost, and limitations for any ML tool you cite.
• Benchmark and claim scrutiny: Evaluates whether reported metrics (e.g., FSC/FRC, Dice/IoU, picking metrics) actually support the biological conclusion you want to make.
• Generalisation and failure-mode realism: Prompts you to assess out-of-distribution risks from differences in defocus, contrast, detectors, pixel size, specimen type, and more.
• Chapter readiness for peer review: Produces technically grounded, non-gestural text suited to ML-literate scientific audiences.

Quick Start

Use the aiml-expert skill to draft a methods-chapter entry for an ML tool by describing its task formulation, architecture family, training paradigm, training-data assumptions, inference requirements, evaluation metric meaning, and known limitations for cryo-EM readers.