shuxiachai

Academic Commercialization Agent

Name: Academic Commercialization Agent
Author: shuxiachai

AI community

SOURCE https://github.com/shuxiachai/academic-commercialization-agent

Description

Turn any research paper into a commercialization report — 6 AI agents, TRL/MRL scoring, patent landscape, market intelligence, verified citations. DeepSeek / OpenAI / Claude.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

Academic Commercialization Assessment Agent

**Turn any research paper or topic into a commercialization readiness report in minutes** — six AI agents gather academic, patent, and market evidence, then produce a scored report with verifiable citations.

[![Tests](https://github.com/shuxiachai/academic-commercialization-agent/actions/workflows/test.yml/badge.svg)](https://github.com/shuxiachai/academic-commercialization-agent/actions/workflows/test.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) [![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/downloads/) [![CrewAI](https://img.shields.io/badge/CrewAI-1.14.x-orange.svg)](https://github.com/crewAIInc/crewAI)

[![Live demo](https://img.shields.io/badge/live%20demo-try%20it-brightgreen.svg)](https://academic-commercialization-agent.up.railway.app)

[Portfolio case study](docs/portfolio-case-study.md) | [90-second demo guide](docs/demo-script-90s.md) | [v2.0.0 release notes](docs/release-v2.0.0.md)

[English](#english) | [中文](#chinese)

English

A multi-agent system built on [CrewAI](https://github.com/crewAIInc/crewAI) that evaluates the commercialization readiness of academic research.

Input a research direction or paper topic. Six specialized AI agents automatically gather evidence from academic literature, patent databases, and market intelligence sources, then produce a structured commercialization assessment report with verifiable citations and a quantitative scorecard.

An optional Decision Context adds the asset, application, decision owner and decision being considered. Topic-only and partial-context runs remain valid, but explicitly stay in orientation mode instead of presenting actor-specific GO/NO_GO advice as if the missing decision boundary had been established.