paper-ranker

Rank candidate papers into selected, ambiguous, and rejected groups with evidence references.

5|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/Dai0-2/Paper_Reach --skill paper-ranker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paper-ranker
Source: https://github.com/Dai0-2/Paper_Reach/tree/main/skills/paper-ranker
Command: npx skills add https://github.com/Dai0-2/Paper_Reach --skill paper-ranker

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a disciplined method to convert a large pool of candidate papers into a clear, reproducible ranking by applying a conservative rubric, turning uncertain results into actionable decisions.

Core Features & Use Cases

  • Conservative ranking: separates papers into selected, ambiguous, and rejected with justification.
  • Evidence-backed decisions: captures reasons and references to support each decision.
  • Reproducible outputs: produces structured results suitable for auditing and handoffs.

Quick Start

Provide a candidate set and run the paper-ranker to produce ranked results with reasons and evidence references.

Frequently Asked Questions about paper-ranker

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

FAQPage Schema
How do I rank research papers with a reproducible rubric?▼

You can rank research papers by applying a conservative rubric to normalized paper records, evidence entries, and inclusion criteria to generate grouped decisions with evidence references.

What is conservative paper ranking for screening candidates?▼

Conservative paper ranking separates screening candidates into selected, ambiguous, and rejected groups by matching topic relevance, method, and dataset against defined rubric thresholds.

How do I get evidence-backed decisions for a literature screening?▼

You can get evidence-backed decisions for literature screening by processing candidate sets with inclusion and exclusion criteria to capture referenced justifications for each ranking outcome.

What inputs do I need for AI-agent paper screening?▼

Paper screening requires normalized paper records, evidence entries, inclusion and exclusion criteria, rubric dimensions, thresholds, and a full-text requirement flag to generate reproducible ranking outputs.

When should I use a conservative rubric for paper ranking?▼

Use a conservative rubric for paper ranking when converting a large pool of uncertain screening candidates into clear, auditable decisions suitable for handoffs and reproducible outputs.