dsh-interview
Run the following command in DeepSeek Harness:
dsh plugin install codingayice/dsh-interview
Paste the following prompt into your AI chat to install this plugin:
Install the dsh-interview plugin in DeepSeek Harness Web by running the command `dsh plugin install codingayice/dsh-interview`, with the full source code available at https://github.com/codingayice/dsh-interview.
About this plugin
Preparing for technical interviews can be daunting, often requiring systematic practice and feedback. dsh-interview, an AI interview practice assistant running in DeepSeek Harness Web, addresses these challenges by offering an intelligent way to help users prepare efficiently. It features five core practice modes: memorizing fundamentals, mock interviews, resume-based question prediction, scenario questions, and LeetCode practice. Throughout the process, the AI provides real-time scoring, commentary, and explanations to help users identify and improve their weaknesses. All practice records are automatically saved, allowing users to track progress, compare responses, and resume unfinished sessions anytime. This plugin is ideal for job seekers preparing for technical development positions, whether they need to review fundamentals, simulate real interviews, predict questions based on their resume, or practice scenarios like system design. It caters especially to those who want dynamic feedback and persistent records beyond static question banks. With local data storage, dsh-interview ensures privacy and convenience, enabling users to practice confidently and continuously enhance their interview skills.
Use Cases
- Preparing for technical development job interviews with simulated practice.
- Reviewing fundamental concepts with AI feedback to improve communication.
- Practicing real-world scenarios like system design and troubleshooting.
Best For
- Job seekers preparing for technical development positions.
- Developers who want to systematically review fundamental concepts.
- Users needing mock interview practice or resume-based question prediction.
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