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aiml-expert

Provides ML/AI guidance for cryo-EM research, including model validation and CapEx estimation.

Updated May 19, 2026
Search Tags:#ai#machine learning#model evaluation#cryo-EM#methods writing#benchmark critique#generalisation
One-click install
npx skills add https://github.com/RRobert92/EM_ML_Segmentation_Tools_Overview --skill aiml-expert
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aiml-expert
Source: https://github.com/RRobert92/EM_ML_Segmentation_Tools_Overview/tree/main/skills/aiml-expert
Command: npx skills add https://github.com/RRobert92/EM_ML_Segmentation_Tools_Overview --skill aiml-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about aiml-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a methods chapter for an ML tool used in cryo-EM?▼

Writing a methods chapter for an ML tool in cryo-EM requires mapping the task formulation, architecture family, training paradigm, and inference constraints into imaging-task language. You must also state known limitations and out-of-distribution risks for readers.

How do I critique ML benchmark claims in a scientific imaging paper?▼

Critiquing ML benchmark claims in scientific imaging involves evaluating whether reported metrics like FSC, FRC, Dice, or IoU actually support the biological conclusion. You must assess metric-to-utility alignment and identify generalisation failure modes.

What is the difference between supervised and self-supervised training for cryo-EM models?▼

Supervised and self-supervised training for cryo-EM models differ in data labeling requirements and learning paradigms. This distinction affects how you describe training data assumptions and evaluate the model architecture family in your methods writing.

How do I assess out-of-distribution risks for machine learning models in electron microscopy?▼

Assessing out-of-distribution risks for machine learning models in electron microscopy involves evaluating differences in defocus, contrast, detectors, pixel size, and specimen type. These factors determine generalisation capabilities and potential failure modes.

Can I use this to evaluate evaluation metrics for AI picking tools in cryo-EM?▼

Evaluating picking metrics for AI tools in cryo-EM is supported by benchmark and claim scrutiny. You can determine whether reported evaluation metrics actually align with the biological utility and conclusions you intend to make.

What limitations should I state when describing an ML architecture for a peer-reviewed methods section?▼

Stating limitations when describing an ML architecture for peer review requires clearly defining inference constraints, training data assumptions, and out-of-distribution considerations. This ensures the text is technically grounded for ML-literate scientific audiences.

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