Interview Prep Agent

Conducts comprehensive mock interviews spanning behavioral, technical, and case study formats with STAR framework coaching, real-time feedback, and company-specific preparation. Simulates realistic interview pressure while providing constructive feedback on answer structure, content depth, communication clarity, and confidence signals. Adapts difficulty based on your target role level and provides actionable improvement plans between sessions.

Supported Platforms & Integrations

Platform	Integration Type	Features
LinkedIn	Profile/Job URL	Pull your experience for answer material; extract job requirements for targeted preparation
Glassdoor	Interview reports	Reference company-specific interview questions and reported difficulty levels
LeetCode	Problem bank	Source technical coding challenges matched to company and difficulty level
Blind (Teamblind)	Community data	Reference salary bands, interview process timelines, and insider tips per company
Levels.fyi	Compensation data	Inform salary negotiation practice with real compensation benchmarks
GitHub	Portfolio review	Analyze your public repositories to prepare for "walk me through your projects" questions

When to Use

• Pre-interview practice: Run 2-3 mock sessions before a real interview to build confidence, refine answers, and identify weak spots
• Behavioral answer crafting: Develop and polish STAR-format answers for common behavioral questions using your real experience
• Technical interview warmup: Practice system design, coding, and architecture questions at the appropriate difficulty for your target level
• Case study preparation: Work through business case frameworks for consulting, product management, or strategy interviews
• Salary negotiation rehearsal: Practice the negotiation conversation with realistic scenarios, objections, and counter-offers
• Company-specific deep dives: Prepare for a specific company's known interview format, values, and culture-fit questions

Alternatives to consider:

• For live mock interviews with human feedback, use Pramp, Interviewing.io, or hire a professional coach
• For pure algorithmic coding practice without interview simulation, use LeetCode or HackerRank directly
• For executive-level interviews requiring industry-specific expertise, work with a specialized recruiter or executive coach

Quick Start

Copy
interview_prep:
  target_company: "Stripe"
  target_role: "Senior Software Engineer"
  interview_type: "behavioral"        # behavioral | technical | case_study | system_design | negotiation
  difficulty: "senior"                 # junior | mid | senior | staff | director
  duration: 45                         # minutes (simulated time)
  your_resume: "./resume/master-resume.md"
  focus_areas:
    - "leadership examples"
    - "conflict resolution"
    - "technical decision-making"
  feedback_style: "detailed"           # brief | detailed | coaching

Example prompt:

Run a behavioral mock interview for a Senior Engineer role at Stripe.
Ask me 5 questions focused on leadership and technical decision-making.
Give me detailed feedback after each answer using the STAR framework.

Example output:

Mock Interview: Senior Software Engineer at Stripe
Type: Behavioral | Duration: 45 minutes | Questions: 5

--- Question 1 of 5 ---

"Tell me about a time you had to make a significant technical
decision with incomplete information. What was the outcome?"

[Stripe context: They value rigorous thinking under uncertainty.
This maps to their engineering principle of 'making reversible
decisions quickly and irreversible ones carefully.']

Take your time to answer. When you're ready, provide your response.

--- After your answer ---

STAR Framework Analysis:
  Situation: [8/10] Clear context — team, timeline, stakes established
  Task:      [6/10] Your specific role was vague. Were you the
             decision-maker or an influencer? Clarify ownership.
  Action:    [7/10] Good description of what you did. Missing:
             what alternatives you considered and why you rejected them.
  Result:    [5/10] Quantify the outcome. "It went well" is weak.
             Better: "Reduced latency by 40%" or "Saved 2 weeks of
             engineering time" or "Zero incidents in 6 months."

  Overall: 6.5/10
  
  Improvement: Add one sentence about trade-offs you considered.
  Stripe interviewers specifically look for evidence of rigorous
  trade-off analysis, not just positive outcomes.

  Ready for question 2? (or would you like to re-try this one?)

Advanced Configuration

Company-specific preparation:

Copy
company_prep:
  company: "Stripe"
  research_areas:
    - values_and_principles           # Stripe's engineering principles
    - recent_products                  # latest launches and initiatives
    - tech_stack                       # Ruby, Go, React, AWS
    - interview_process                # stages, timeline, panel format
    - culture_signals                  # what interviewers look for
  known_questions:                     # from Glassdoor/Blind reports
    - "Why Stripe?"
    - "Design a payment processing system"
    - "Tell me about a time you disagreed with your manager"

Feedback calibration:

Copy
feedback:
  style: "coaching"                    # coaching | evaluative | encouraging
  scoring: true                        # numeric scores per STAR element
  show_ideal_answer: false             # show example strong answer after yours
  track_improvement: true              # compare across sessions
  focus_on_weakest: true               # extra feedback on lowest-scoring areas
  body_language_tips: true             # communication delivery suggestions

Full parameter reference:

Parameter	Type	Default	Description
target_company	string	generic	Company to tailor questions and evaluation criteria for
target_role	string	required	Job title you are interviewing for
interview_type	string	behavioral	Format: behavioral, technical, case_study, system_design, negotiation
difficulty	string	mid	Level: junior, mid, senior, staff, director
duration	integer	45	Simulated interview length in minutes
num_questions	integer	5	Number of questions per session
your_resume	string	null	Path to resume for personalized answer suggestions
feedback_style	string	detailed	Feedback depth: brief, detailed, coaching
allow_retries	boolean	true	Allow re-answering questions after feedback
show_ideal_answer	boolean	false	Display an example strong answer for comparison
track_progress	boolean	true	Save scores across sessions for improvement tracking

Core Concepts

Concept	Description
STAR Framework	Situation-Task-Action-Result: the standard structure for behavioral answers. Each element is scored independently because interviewers evaluate each component separately.
Competency Mapping	Each question targets a specific competency (leadership, collaboration, technical judgment). The agent maps your answers to competencies to identify gaps across your full interview story set.
Answer Library	A personal collection of polished STAR stories from your experience, tagged by competency. One strong story can answer 3-4 different questions with slight reframing.
Difficulty Calibration	Questions scale with target level: junior roles get "tell me about a time you learned something new"; staff roles get "tell me about a time you influenced org-wide technical strategy."
Company Signal Matching	Each company values different signals. Amazon values customer obsession; Google values analytical rigor; Stripe values clarity of thought. Feedback is calibrated to what the target company's interviewers reward.

              Interview Prep Session Flow

  Preparation          Mock Interview        Post-Session
  +-----------+     +------------------+    +-------------+
  | Company   |---->|                  |--->| Score Card  |
  | Research  |     | Question Asked   |    | (per STAR)  |
  +-----------+     |       |          |    +-------------+
                    |       v          |          |
  +-----------+     | Your Answer      |    +-------------+
  | Resume    |---->|       |          |--->| Improvement |
  | Analysis  |     |       v          |    | Plan        |
  +-----------+     | STAR Analysis    |    +-------------+
                    |       |          |          |
  +-----------+     |       v          |    +-------------+
  | Role      |---->| Detailed Feedback|--->| Answer      |
  | Level     |     | (with coaching)  |    | Library     |
  +-----------+     +------------------+    +-------------+
                           |
                    Retry option
                    (re-answer with tips)

Workflow Examples

Scenario 1: Full behavioral interview simulation

Input:  "Run a complete 5-question behavioral interview for a Product
         Manager role at Google. Focus on analytical thinking and
         cross-functional collaboration."

Output: Mock Interview Session — Google PM, Behavioral
        
        Q1: "Tell me about a time you used data to influence a
             product decision that stakeholders initially resisted."
        [Your answer -> STAR feedback -> retry option]
        
        Q2: "Describe a situation where you had to align engineering,
             design, and business teams on a controversial trade-off."
        [Your answer -> STAR feedback -> retry option]
        
        Q3: "Give an example of a product launch that didn't go as
             planned. What did you learn?"
        [Your answer -> STAR feedback -> retry option]
        
        Q4: "How did you prioritize when you had three critical
             features and resources for only one?"
        [Your answer -> STAR feedback -> retry option]
        
        Q5: "Tell me about your most impactful contribution to
             user growth or retention."
        [Your answer -> STAR feedback -> retry option]
        
        Session Summary:
          Average STAR score: 7.2/10
          Strongest: Situation setup (8.5 avg)
          Weakest: Result quantification (5.8 avg)
          Competency gaps: Need more stories about failure/learning
          Next session focus: Practice quantifying outcomes

Scenario 2: System design interview practice

Input:  "Practice a system design interview: 'Design a URL shortener
         like bit.ly' at senior engineer level."

Output: System Design: URL Shortener
        Time: 45 minutes | Level: Senior
        
        I'll play the interviewer. Let's start:
        
        "Design a URL shortening service. Start with requirements."
        
        [Interactive session where the agent asks follow-up questions:
         - "What's your estimated QPS?"
         - "How do you handle hash collisions?"
         - "What happens if a shortened URL goes viral?"
         - "How would you implement analytics?"
         - "Walk me through the read path latency."]
        
        Session Feedback:
          Requirements gathering:  8/10 — Asked good clarifying Qs
          High-level design:       7/10 — Solid, missed caching layer
          Deep dive:               6/10 — Hash function choice was
                                   hand-wavy. Research Base62 vs MD5.
          Scalability:             7/10 — Good horizontal scaling plan.
                                   Missing: database sharding strategy.
          Communication:           8/10 — Clear explanations, good
                                   use of diagrams (ASCII was clear).

Scenario 3: Salary negotiation rehearsal

Input:  "Practice salary negotiation for a Staff Engineer offer from
         Datadog. They offered $280K base, $150K RSU/year, $50K sign-on."

Output: Negotiation Practice Session
        
        Context loaded:
          Datadog Staff Eng market range (Levels.fyi):
            Base: $250K-$320K | RSU: $120K-$200K/yr
          Your offer: Base $280K (50th %ile), RSU $150K (45th %ile)
        
        I'll play the recruiter. Let's begin:
        
        Recruiter: "We're excited to extend this offer. We think
        it's very competitive for the role. How do you feel about it?"
        
        [Interactive negotiation simulation]
        
        After your response, feedback on:
          - Did you express enthusiasm before negotiating?
          - Did you anchor to a specific number or range?
          - Did you justify with market data or competing offers?
          - Did you negotiate base and RSU separately?
          - Did you avoid ultimatums while being firm?
        
        Coaching notes:
          "Never say 'I was hoping for more.' Instead: 'Based on my
          research and the scope of this role, I'd be looking for
          $310K base and $180K RSU to accept immediately.' Give them
          a clear path to yes."

Best Practices

1. Build an answer library of 8-12 stories: Most behavioral interviews ask 4-6 questions. Having 8-12 polished STAR stories covering leadership, conflict, failure, innovation, and collaboration means you always have a relevant answer. Tag each story by the competencies it demonstrates.

2. Practice out loud, not just in your head: Reading your answer silently feels different from speaking it. Use the mock interview to practice verbal delivery. Notice where you ramble, pause too long, or lose your thread. Verbal practice builds the muscle memory that reduces interview anxiety.

3. Always quantify results: "Improved performance" is forgettable. "Reduced API latency from 800ms to 120ms, improving user retention by 12%" is memorable and credible. If you do not have exact numbers, use reasonable estimates with "approximately" — it still demonstrates result-orientation.

4. Research the company before every session: Configure the agent with the target company so questions and feedback are calibrated to that company's known interview style, values, and culture. Generic preparation produces generic answers that do not stand out.

5. Do post-session reviews within 24 hours: After each mock session, review the feedback and revise your answer library. The improvement plan is only valuable if you act on it before the next session. Space sessions 2-3 days apart to allow for deliberate practice.

Common Issues

Answers are too long and rambling Set a target of 90-120 seconds per behavioral answer. Practice with a timer. The STAR framework naturally constrains length: 1-2 sentences for Situation, 1 sentence for Task, 3-4 sentences for Action, 1-2 sentences for Result. If you consistently exceed 2 minutes, your Situation setup is likely too detailed.

Cannot think of relevant stories for specific questions This is an answer library gap, not a thinking problem. Before mock interviews, brainstorm stories by competency rather than by question. List every project, conflict, achievement, and failure from the last 5 years. Tag each with competencies. You will find that one story covers multiple question types with slight reframing.

Technical interview feedback feels too easy or too hard Adjust the difficulty parameter to match your target level precisely. A "senior" system design question expects distributed systems knowledge; a "mid" level expects solid fundamentals. If the current level is still mismatched, provide additional context about your specific technical background.

Privacy & Data Handling

• Interview answers stay local: All your mock interview responses, feedback, and answer library are stored only on your local filesystem. Nothing is transmitted to external services.
• Resume data is local: If you provide your resume for personalized answer suggestions, it is read from your local file and not stored or shared externally.
• Company research: Company-specific information used for question tailoring comes from publicly available sources. No proprietary or insider company data is accessed.
• Progress tracking: Session scores and improvement plans are saved to your local config directory. No performance data is shared with potential employers or third parties.
• Safe for confidential preparation: You can practice with real company names, real offer numbers, and real experiences without concern about data leakage. Everything stays on your machine.