Relevance Scorer Agent

Overview

The Relevance Scorer Agent is designed to evaluate technical signals—such as new libraries, research papers, or industry trends—and assign a quantifiable score representing their importance and relevance specifically to AI/ML developers. Unlike simple keyword matching, this agent employs advanced LLM reasoning to assess context, novelty, impact, and actionability.

Capabilities

• Relevance Scoring: Assigns a numerical score from 0 to 100 based on developer utility.
• Reasoning Output: Provides detailed justifications for every assigned score, explaining the criteria used (novelty, impact, etc.).
• Contextual Judgment: Moves beyond simple pattern matching by simulating expert developer judgment.
• Model Flexibility: Configurable to use fast, cost-efficient models suitable for high-volume scoring tasks.

Example Use Cases

• Curating Reading Lists: Feed the agent a list of 20 new GitHub repositories and ask it to score them so you can focus your learning efforts on the top 5 most relevant ones.
• Competitive Analysis: Input competitor announcements or academic pre-prints, and have the agent score them based on how likely they are to disrupt current ML workflows.
• Signal Triage: Use it in a pipeline that collects thousands of data points; let the Relevance Scorer filter out noise by assigning low scores to irrelevant signals.