research-workflow

Guide AI/ML research from hypothesis generation to report generation.

Updated Mar 13, 2026

Search Tags:#research #mlops #experiment design #evaluation #literature review #hypothesis

Ready to run

npx skills add https://github.com/TECHKNOWMAD-LABS/cortex-research-suite --skill research-workflow-techknowmad-labs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-workflow
Source: https://github.com/TECHKNOWMAD-LABS/cortex-research-suite/tree/main/skills/research-workflow
Command: npx skills add https://github.com/TECHKNOWMAD-LABS/cortex-research-suite --skill research-workflow-techknowmad-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured framework for designing, planning, and executing AI/ML research projects, ensuring scientific rigor and reproducibility from hypothesis to results.

Core Features & Use Cases

Hypothesis Formulation: Guides users in creating clear, falsifiable research hypotheses.
Experimental Design: Outlines principles for robust experimental setups, including control variables and ablation studies.
Literature Review: Provides a phased workflow for conducting comprehensive literature reviews.
Evaluation Framework: Helps define appropriate metrics and benchmarks for assessing research outcomes.
Use Case: A researcher needs to design an experiment to test a new model architecture. This Skill will guide them through defining the hypothesis, setting up control variables, choosing evaluation metrics, and planning the experiment structure.

Quick Start

Use the research-workflow skill to help design an experiment for improving transformer efficiency.

Frequently Asked Questions about research-workflow

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

FAQPage Schema

How do I design an AI experiment to test a new model architecture?▼

Designing an AI experiment requires guiding hypothesis formulation, establishing robust experimental setups with control variables, and defining evaluation metrics. This framework supports end-to-end project planning from initial literature review to evidence gathering and ablation studies.

What's the best way to structure a literature review for machine learning research?▼

Structuring a literature review for machine learning research requires a phased workflow to ensure comprehensive evidence gathering. This framework provides structured research planning methodologies to systematically evaluate existing literature and define appropriate evaluation benchmarks.

How do I formulate a falsifiable research hypothesis for an ML project?▼

Formulating a falsifiable research hypothesis for an ML project involves creating clear, testable statements about model behavior. This framework guides you through structured hypothesis generation, ensuring scientific rigor before proceeding to experimental design and evaluation framework definition.

Can I automate my research pipeline execution and artifact generation?▼

Automating research pipeline execution and artifact generation is supported through integrated research pipeline scripts. This framework facilitates automated execution of experimental designs, connecting evidence gathering and analysis directly to report generation outputs.

Does this research workflow framework support MLOps practices?▼

This research workflow framework supports MLOps practices by integrating structured experimental design with automated research pipeline scripts. It ensures reproducibility from hypothesis generation to report generation, aligning with MLOps evaluation frameworks and artifact tracking.

What evaluation metrics should I use to assess my AI research outcomes?▼

Assessing AI research outcomes requires defining appropriate metrics and benchmarks tailored to your specific experimental design. This framework helps establish an evaluation framework that measures evidence gathering results against your initial research hypothesis and control variables.