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Novelty Verification Research
Rigorous academic and internal research verification to score idea novelty and identify prior work.
$7
novelty-verification-research
Example session with this skill installed
Assess the novelty of this idea: using sparse LoRA routing for edge-device language models where adapters are activated based on local energy budget and task complexity. Score it and identify the closest prior work.
- Read your context and instructions
- Compiled the novelty-verification-research
The skill extracts a method signature covering what the idea does, how it works and what it claims as novel. It generates keyword groups, searches available sources, identifies the closest three to five prior works with a comparison table, produces a differentiation analysis, assigns a conservative novelty score with a label and rationale, and delivers a recommendation to proceed, modify or abandon. All source entries are traceable. Confidence limitations are documented where source coverage is incomplete.
Connects securely to your tools. The creator never sees your data.
About this skill
Overview
The Novelty Verification Research skill provides a rigorous framework for assessing the originality of research ideas, methods, and manuscripts. Designed for R&D teams and academic researchers, it acts as an automated "sanity check" to prevent duplicating existing work and identifies exactly where a new concept stands in relation to the state-of-the-art.
What it does
This skill performs a multi-source comparative analysis by searching academic databases, preprint servers like arXiv, and internal knowledge bases. It goes beyond simple keyword matching to extract a "method signature" from your proposal, comparing it against prior work based on architecture, data, and objectives.
- Generates a conservative novelty score (1-5) based on evidence.
- Lists the 3-5 closest prior works with direct comparison tables.
- Performs internal anti-repetition checks against your team's failed or in-progress ideas.
- Provides a clear recommendation: Proceed, Modify, or Abandon.
Why use this skill
Writing a manual literature review is time-consuming and prone to bias. This skill provides a structured, objective perspective that prevents the "innovation tunnel vision" by searching across multiple repositories simultaneously. It ensures that before you invest resources into a project, you have a verifiable map of the existing research landscape.
Output
The result is a structured Novelty Report including a method signature, a differentiation analysis table, confidence scores, and specific next-step recommendations to strengthen the idea's unique contribution.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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