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research-pipeline

Orchestrate multi-stage research discovery with AI agents for exploration, ranking, and peer review.

Updated Feb 5, 2026
Search Tags:#research#discovery#pipeline#peer review#ranking#topic exploration
One-click install
npx skills add https://github.com/mbed92/phd --skill research-pipeline-mbed92
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-pipeline
Source: https://github.com/mbed92/phd/tree/main/.claude/skills/research-pipeline
Command: npx skills add https://github.com/mbed92/phd --skill research-pipeline-mbed92

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of exploring a research topic, identifying key focus areas, ranking their importance, and obtaining peer review feedback, streamlining the initial stages of academic or R&D work.

Core Features & Use Cases

  • Automated Research Exploration: Identifies and summarizes key focus areas within a given research topic.
  • Intelligent Ranking: Ranks focus areas based on defined criteria using an Elo-based tournament.
  • Peer Review Simulation: Provides simulated peer review and refinement suggestions for the ranked areas.
  • Use Case: A researcher can input a broad topic like "AI in healthcare" and receive a prioritized list of sub-topics, along with potential research questions and reviewer feedback, significantly accelerating their literature review and proposal development.

Quick Start

Use the research-pipeline skill to explore the topic of "haptic perception".

Frequently Asked Questions about research-pipeline

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

FAQPage Schema
How do I automate research topic discovery and ranking for literature reviews?▼

Automating research topic discovery involves using an AI pipeline to explore broad subjects, rank sub-topics via Elo tournaments, and simulate peer review. This Skill orchestrates those sequential stages to generate prioritized research questions and refinement feedback.

What is the best way to simulate peer review for early stage research hypotheses?▼

Simulating peer review for research hypotheses involves using AI agents to evaluate and provide refinement suggestions for ranked topics. This Skill includes a dedicated simulation stage with web-based validation to generate actionable reviewer feedback.

How does an Elo tournament ranking system work for evaluating research topics?▼

An Elo tournament ranking system for research topics evaluates sub-topics against each other in simulated matches to assign dynamic ratings. This Skill uses intelligent ranking agents to systematically score and prioritize focus areas based on defined criteria.

Can I use automated research discovery for any broad academic field?▼

Yes, automated research discovery can be applied to any broad academic field or R&D topic. The Skill identifies and summarizes key focus areas within a given subject, making it suitable for accelerating proposal development across various research landscapes.

Do I need to configure multiple AI agents to explore and rank research topics?▼

No, you do not need to manually configure multiple AI agents to explore and rank research topics. This Skill automatically manages sequential data handoffs and tool permissions across distinct exploration, ranking, and review stages.

What are the limitations of using simulated peer review for academic research?▼

Simulated peer review provides automated refinement suggestions but cannot replace human expert validation. This Skill is designed to streamline initial academic work and accelerate literature reviews, serving as a preliminary feedback mechanism rather than a final approval.

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