What is the interview simulator used for?

To simulate a realistic coding interview with time constraints, hints, and evaluation prompts.

Hints escalate from directional to algorithm to implementation as you request more help.

Can I customize difficulty and time settings?

Yes. The input schema lets you set difficulty, timeLimit, includeFollowups, and companyStyle.

interview-simulator

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npx machina-cli add skill a5c-ai/babysitter/interview-simulator --openclaw

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SKILL.md

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Interview Simulator Skill

Purpose

Simulate a realistic coding interview experience with time constraints, hints, follow-ups, and evaluation.

Capabilities

Time-boxed problem presentation
Hint system with escalation
Follow-up question generation
Communication evaluation prompts
Realistic interviewer responses
Performance tracking

Target Processes

mock-coding-interview
behavioral-interview-prep
faang-interview-prep

Interview Simulation Flow

Problem Presentation: Present problem with constraints
Clarification Phase: Answer clarifying questions
Approach Discussion: Evaluate proposed approach
Implementation Phase: Monitor coding progress
Testing Phase: Discuss test cases
Optimization Phase: Explore improvements
Follow-up Questions: Present variations

Hint Escalation System

Level 1: Direction hint (no algorithm reveal)
Level 2: Approach hint (mention technique)
Level 3: Algorithm hint (name the approach)
Level 4: Implementation hint (key insight)

Input Schema

{
  "type": "object",
  "properties": {
    "problemId": { "type": "string" },
    "difficulty": { "type": "string", "enum": ["easy", "medium", "hard"] },
    "timeLimit": { "type": "integer", "default": 45 },
    "includeFollowups": { "type": "boolean", "default": true },
    "companyStyle": { "type": "string" }
  },
  "required": ["difficulty"]
}

Output Schema

{
  "type": "object",
  "properties": {
    "success": { "type": "boolean" },
    "problem": { "type": "object" },
    "hints": { "type": "array" },
    "followups": { "type": "array" },
    "evaluation": { "type": "object" }
  },
  "required": ["success"]
}

Source

git clone https://github.com/a5c-ai/babysitter/blob/main/plugins/babysitter/skills/babysit/process/specializations/algorithms-optimization/skills/interview-simulator/SKILL.md

View on GitHub

Overview

Simulates a realistic coding interview experience with time limits, hints, follow-ups, and evaluation. It guides you through problem presentation, clarifications, implementation, testing, and optimization, mirroring FAANG-style interviews.

How This Skill Works

The system presents a time-boxed problem and records your progress through the seven-phase flow: problem presentation, clarification, approach discussion, implementation, testing, optimization, and follow-ups. Hints escalate from directional to algorithm to implementation, while realistic interviewer responses and evaluation prompts track performance.

When to Use It

Mock coding interviews with time constraints and real-time evaluation
FAANG-style interview prep with hint escalation and follow-ups
Clarification and approach discussion practice to improve communication
Behavioral interview prep embedded in technical simulations
Performance tracking across multiple sessions to measure improvement

Quick Start

Step 1: Configure problem difficulty and timeLimit, then start the session
Step 2: Work through the seven-phase interview flow, using hints as needed and noting follow-ups
Step 3: Review the evaluation feedback and adjust your practice goals

Best Practices

Configure difficulty and timeLimit to mirror your target interview
Use the hint escalation levels to push toward algorithmic thinking and solid implementation
Follow the seven-phase flow (presentation, clarifications, approach, implementation, testing, optimization, follow-ups) as a repeatable routine
Practice with follow-up questions to simulate interviewer variability
Review the evaluation prompts to improve communication and problem-solving justification

Example Use Cases

A software engineer practices a medium-difficulty problem under a 45-minute session with structured hints and an evaluation summary
An applicant uses follow-ups to explore alternative approaches and edge cases during a mock interview
FAANG-style prep session featuring realistic interviewer responses and performance tracking
Behavioral questions are integrated into the technical session to simulate cross-questioning
Multiple sessions are completed to measure improvements in solution quality and communication clarity

Frequently Asked Questions

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