research-deep-dive

Analyzes research topics and papers to identify assumptions, failure modes, and propose validation experiments.

1|Updated Apr 12, 2026

Search Tags:#experiment design #technical research #research analysis #method comparison #evidence-based analysis

Ready to run

npx skills add https://github.com/KirillKlem/codex-skills --skill research-deep-dive-kirillklem

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-deep-dive
Source: https://github.com/KirillKlem/codex-skills/tree/main/skills/research-deep-dive
Command: npx skills add https://github.com/KirillKlem/codex-skills --skill research-deep-dive-kirillklem

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users understand complex research topics, papers, methods, and technical directions through in-depth analysis and evidence-based conclusions.

Core Features & Use Cases

In-depth Research Analysis: Analyze research topics, papers, and methods for robust evidence and conclusions.
Taxonomy of Approaches: Organize relevant approaches into a structured framework.
Assumptions and Failure Modes: Identify and analyze assumptions and potential failure modes of methods.
Comparison with Adjacent Methods: Compare the target method with neighboring alternatives.
Experiments to Validate: Propose experiments to validate applicability and claims.
Use Case: If you are considering implementing a new machine learning algorithm, use this Skill to understand its effectiveness, assumptions, and potential pitfalls.

Quick Start

Run the research-deep-dive skill on the paper titled 'Deep Learning for Image Recognition'.

Frequently Asked Questions about research-deep-dive

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

FAQPage Schema

How do I analyze a research paper for hidden assumptions and failure modes?▼

To analyze a research paper for failure modes, conduct an evidence-oriented technical assessment to identify core ideas, extract underlying assumptions, and compare them with adjacent methods. This process reveals structural weaknesses and potential pitfalls in the target methodology.

What is the best way to compare a new machine learning method with adjacent approaches?▼

Comparing a new machine learning method with adjacent approaches requires organizing relevant techniques into a structured taxonomy. This comparison highlights the target method's distinct assumptions, potential failure modes, and relative effectiveness based on evidence-based conclusions.

How do I design experiments to validate claims made in technical research?▼

Designing experiments to validate technical research claims involves proposing targeted tests that assess the applicability of the method. This requires analyzing the paper's core ideas and structuring experiments to specifically test the identified assumptions and failure modes.

Can I use technical research analysis to structure a literature review for method comparison?▼

Yes, technical research analysis organizes relevant approaches into a structured framework, creating a taxonomy of methods. This structured literature review allows you to systematically compare the target method with neighboring alternatives and evaluate evidence.

When do I need evidence-based analysis for evaluating technical research directions?▼

Evidence-based analysis for evaluating technical research is needed when considering implementing new algorithms or methods. It helps you understand complex topics by identifying core ideas, testing assumptions, and proposing experiments to validate applicability before adoption.