An intelligent multi-stage interview agent that manages technical profiles, JD matching, and structured 5-question mock interview cycles with real-time scoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-interview-assistant
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-interview-assistant using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Interview Assistant?
The AI Interview Assistant is a sophisticated simulation system designed to replicate real-world technical interview scenarios. Operating within the Openclaw Skills ecosystem, it utilizes a multi-layered architecture—from high-level orchestration to atomic tools—to guide candidates through a professional evaluation process. Whether you are practicing for a specific job or general skill improvement, the assistant provides a rigorous environment to test your knowledge.
This skill stands out by integrating a unique feedback loop: it combines 70% keyword matching with 30% logical flow assessment to ensure users receive objective and actionable insights. By leveraging technical knowledge bases and web-fetched current industry trends, it offers a dynamic range of questions covering LLM fundamentals, distributed training, RAG/Agents, and more.
AI Interview Assistant Use Cases
- Technical interview preparation for software engineering and AI-specialized roles.
- Real-time gap analysis between a candidate's current profile and a specific Job Description (JD).
- Anonymous practice sessions for developers to assess their technical proficiency without data exposure.
- Automated generation of technical evaluation reports to track learning progress over time.
How AI Interview Assistant Works
- Profile Management: The agent initializes by creating a technical persona or querying existing profiles from the local database.
- JD Matching: If a Job Description is provided, the system analyzes the alignment between the user's technical stack and the role requirements.
- Interview Execution: A structured cycle of 5 questions is triggered, pulling from GitHub knowledge bases and live web sources.
- Real-time Scoring: Every answer is processed via tool_scorer, providing immediate feedback on keyword coverage and logical depth.
- Follow-up Logic: The assistant can perform up to 3 rounds of follow-up questions per topic if the initial score is below 60%.
- Summary & Archiving: A final report is generated, grading the performance from S to D and updating the user's persistent profile with focus areas for improvement.
AI Interview Assistant Setup
To integrate this capability into your environment as part of your Openclaw Skills collection, use the following commands:
# Install the interview assistant dependencies
npm install @openclaw/interview-engine
# Configure the local resume database
openclaw setup --skill ai-interview-assistant
# Start the simulation via terminal or agent interface
# Simply say: "I want to start a mock interview"
AI Interview Assistant Data Schema & Taxonomy
The skill manages data across four primary dimensions to ensure a comprehensive evaluation flow:
| Data Component | Format | Description |
|---|---|---|
| User Profile | Local JSON | Stores technical stack tags, previous scores, and feedback history. |
| Scoring Matrix | Weighted Table | 70% Keyword Matching, 30% Logical Flow and Depth. |
| Question Bank | MD/JSON | Sourced from GitHub (wdndev/llm_interview_note) and web fetching. |
| Final Report | Markdown | Structured summary including Avg Score, Grade (S-D), and Strong/Weak points. |
AI Interview Assistant Advanced Features
- Multi-Layer Orchestration: Uses an L1-L3 architectural model for seamless state transitions and tool execution.
- Anonymous Mode: Complete privacy support where no personal identification is required for the session.
- Signal-Driven Workflow: The system uses handshake signals like SUCCESS and TERMINATE to ensure reliable state management.
- Dynamic Question Sourcing: Blends traditional 'standard' interview questions (50%) with real-world scenarios (30%) and cutting-edge tech trends (20%).
SKILL.md
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