In the DeepSeek Harness (DSH) plugin ecosystem, tools that target a specific research domain can significantly improve research efficiency. For users working on asset pricing or machine learning, the workflow from literature collection to final report generation is often cumbersome and time-consuming. z-asset/za_report aims to address this pain point by integrating various stages of academic writing into the DSH workflow.

Plugin Positioning

This is an academic writing plugin maintained by Z-Asset, focusing on asset pricing and machine learning/deep learning research. It packages features such as literature review, data processing, model training, and report generation, serving empirical asset pricing research (such as factor models and cross-sectional return forecasting) as well as machine learning application scenarios.

Core Features

The plugin provides the following main capabilities:

  1. Literature review: Assists in organizing related papers and providing literature support.
  2. Data processing: Assists in finding data or evaluating datasets.
  3. Model training: Supports regression analysis, machine learning model training, and cross-sectional return forecasting.
  4. Report writing: Supports paper writing and drafting sections.
  5. PPT creation: Supports generating presentation slides, Beamer presentations, and Job Market Talks.

Installation and Activation

Installing the plugin requires the official command. Before running it, make sure your DSH environment is configured correctly.

dsh plugin --profile <你的profile> add zresearch-report

After installation, the plugin automatically loads the corresponding skill. You do not need to manually configure additional files.

Usage

In a DSH session, you can simply ask in natural language to trigger the corresponding feature. The plugin will automatically invoke the background worker and critic roles based on the task.

  • Literature review: Ask “help me do a literature review / find related papers”
  • Data processing: Ask “help me find data / evaluate a dataset”
  • Model training: Ask “run regression / train an ML model / cross-sectional return forecasting”
  • Report writing: Ask “write a paper / draft a section”
  • PPT creation: Ask “create presentation slides / Beamer / job market talk”

Technical Structure

Internally, the plugin is organized using a skill structure, including the main skill definition and a references directory. The references directory defines the worker/critic roles and calibration settings for the asset pricing + ML/DL domain (domain-profile). Its core dependencies include @deepseek-ai/cordis and @deepseek-ai/dsh-skill, and it is released under the MIT license.

Notes

  • Runtime permissions: The plugin runs with the permissions of the current DSH process. Please check the source code and license before installation.
  • Target audience: Primarily for asset pricing and machine learning researchers who need to write academic papers or presentation materials.

Summary

By standardizing the academic writing workflow, z-asset/za_report reduces the intermediate steps between data processing and document generation. For researchers who need to frequently produce high-quality reports, this is a practically deployable tool. For more details, consult the plugin directory or GitHub repository.