StudyMate: A DSH Learning Assistant for Mathematics and Computer Science Subjects

Preface

In DSH’s “everything is a plugin” ecosystem, community catalogs have no official affiliation with DeepSeek or Huanfang. Learning mathematics and computer science typically requires systematic knowledge organization and hands-on practice. StudyMate aims to solve the problems of how to efficiently plan a learning path, manage learning progress, and conduct practical evaluation.

What It Is

StudyMate is an assistant for learning mathematics and computer science, with the core principle of “learn with doing”. The plugin is maintained by Miaotofu01, currently has 936 Stars, and is released under the MIT license.

Core Features

  • Course Overview and Roadmap: Provides a subject overview, displays a knowledge-point roadmap, and supports coloring by status.
  • Courseware Is the Main Carrier of Learning: Courseware serves as the primary object of learning and includes explanations, exercises, and project-based practice.
  • Real-Time Rendering in the Reading Client: HTML is no longer pre-generated; formulas, figures, and questions are rendered in real time in the reading client.
  • Cross-Subject Shared Memory: Remembers the learner’s current level, effective explanation styles, and common sticking points to enable knowledge reuse.
  • Supports DSH Learning-Mode Presets: Deeply integrates with DSH’s learning modes.
  • Supports the Google Antigravity Plugin: Supports the Antigravity multi-agent plugin (only v1.0.0 is supported).
  • Supports Codex and ChatGPT Work Plugins: Supports the OpenAI plugin ecosystem (only v1.0.0 is supported).
  • Customizable Explanation Style: Adjusts explanation style based on the learner’s preferences.
  • Collects Latest Materials and Standards: Can collect the latest materials and standards.
  • On-the-Spot Question Generation and Evaluation: Supports generating questions on the spot and evaluating responses.
  • Explanation Switching for Learning Preferences: Provides an explanation-switching feature for cases such as “too easy” or “I did not understand”.

Installation and Activation

After installing the plugin, study data is stored by default in the ~/StudyMate directory. The engine has been migrated from Python to TypeScript, so a Python environment is no longer required.

  1. Install the plugin (using the Web client as an example):
dsh plugin --profile web add @yunmiao/studymate
  1. Update the plugin:
dsh plugin --profile web update @yunmiao/studymate@latest
  1. Enable learning mode:
    When creating a new session, select “Learning Mode”. Switch from the sidebar to the reading client to study, answer questions, and retrieve materials.

Typical Usage

  • Direction Exploration: Enter “I do not know what to learn; help me choose a direction”, and the system provides exploration suggestions.
  • Specify a Goal: Enter “I want to learn C++ for competitions”, and the system generates an outline based on the goal, level, and other information.
  • Break Through Difficult Points: Paste text you do not understand and point out “I did not understand this”, and the system provides targeted explanation.
  • Self-Assessment: Enter “Quiz me”, and the system generates questions on the spot and evaluates your responses.
  • Feedback Adjustment: If the content feels “too easy” or “I did not understand”, the system adjusts the explanation style.

Applicable Scenarios and Notes

  • Applicable Scenarios: Suitable for learners who need systematic study of mathematics, computer science, or related subjects and want to combine theory with practice.
  • Version Note: The Codex and ChatGPT Work plugins and the Google Antigravity plugin only support v1.0.0; later versions are not guaranteed to be available.
  • Environment Requirements: A Python environment is no longer required; only Node.js is needed.
  • Data Location: Study data is stored by default in ~/StudyMate; the example workspace is no longer included in the repository.

Conclusion

StudyMate attempts to transform AI-assisted learning from fragmented conversations into systematic “Learn with doing” through structured workflows and memory mechanisms. For more details and source code, see the community catalog or the GitHub repository.

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