khetansarvesh/job-interview-prep

Interview Preparation

Generate a comprehensive, company-specific interview preparation guide. Researches the interview process, maps your experience to likely questions, and identifies gaps.

When to Activate

• User says "prepare me for an interview at [company]"
• User has an upcoming interview and wants to prep
• User asks for STAR stories for a specific role
• User wants to know what to expect in interviews at a company

Prerequisites

• Notion (fetch via python3 scripts/notion/page_reader.py resume) — work experience and skills
• Notion (fetch via python3 scripts/notion/page_reader.py projects) — project summaries and answer snippets

Non-Negotiables

1. Never fabricate stories — every STAR response must trace to real experience in the user's profile
2. Always cite sources for interview process data (Glassdoor, Blind, LeetCode Discuss, etc.)
3. Flag gaps honestly — if the user has no matching story for a likely question, say so and suggest how to bridge
4. Tailor to the specific role — generic prep is useless. Every question and story must connect to the JD
5. Label inferred questions — every question NOT sourced from a real candidate report must be labeled [inferred from JD]. Never attribute inferred questions to Glassdoor or Blind.
6. Never fabricate statistics — never invent Glassdoor ratings, experience percentages, or interview statistics. If data is unavailable, write "unknown — not enough data"

Workflow

Step 1: Gather Context

1. Read Notion (fetch via python3 scripts/notion/page_reader.py resume) and Notion (fetch via python3 scripts/notion/page_reader.py projects)
2. If an evaluation report exists for this company/role, read it for archetype and gap analysis
3. Ask the user for any additional context:
   • Which round is it? (phone screen, technical, behavioral, onsite, final)
   • Do they know who's interviewing them?
   • Any specific topics they're worried about?

Step 2: Research Interview Process

Use WebSearch to find interview intel. Run these queries:

| Query | What to extract | | ------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | | "{company}" "{role}" interview questions site:glassdoor.com | Actual questions asked, difficulty rating, experience rating, process timeline, number of rounds, offer/reject ratio | | "{company}" interview process site:teamblind.com | Candid process descriptions, recent data points, comp negotiation details, hiring bar | | "{company}" "{role}" interview site:leetcode.com/discuss | Specific coding/technical problems, system design topics, round structure | | "{company}" engineering blog | Tech stack, values, what they publish about, technical priorities | | "{company}" interview process {role} | General fallback — fills gaps from above: blog posts, YouTube, prep guides, candidate write-ups |

If the company is small or obscure and yields few results, broaden: search for the role archetype at similar-stage companies, and note that intel is sparse.

Extract and organize:

Process Overview:

| Field | Details | | ------------------------ | --------------------------------------------------------------------- | | Rounds | (number and types: phone, technical, system design, behavioral, etc.) | | Timeline | (typical days/weeks from first contact to offer) | | Difficulty | (1-5 based on reported experiences) | | Positive Experience Rate | (% from Glassdoor if available, or "unknown") | | Known Quirks | (anything unusual about their process) | | Sources | (links to Glassdoor, Blind, etc.) |

If data is insufficient for any field, write "unknown — not enough data" rather than guessing.

Step 3: Round-by-Round Breakdown

For each interview round:

### Round {N}: {Type}

- **Duration:** {X} min
- **Conducted by:** (peer / manager / skip-level / recruiter — if known)
- **What's evaluated:** (coding, system design, behavioral, culture fit)
- **Reported questions:**
  - {question} — [source: Glassdoor 2026-Q1]
  - {question} — [source: Blind]
  - {question} — [inferred from JD]
- **How to prepare:** (1-2 concrete actions)

If round structure is unknown, state that and provide the best available intel on what types of rounds to expect based on company size, stage, and role level.

Step 4: Question Categories

Organize all discovered and inferred questions into 4 categories. For sourced questions, cite: [source: Glassdoor 2026-Q1]. For questions inferred from JD analysis, label: [inferred from JD].

Technical Questions:

• System design (relevant to role: ML pipelines, agent architectures, RAG systems, etc.)
• Coding (algorithms, data structures, ML implementation)
• Domain-specific (NLP, CV, distributed training, inference optimization — whatever the role requires)
• For each: the question, source, and what a strong answer looks like for THIS candidate (reference specific CV proof points)

Behavioral Questions:

• Leadership and teamwork
• Conflict resolution
• Failure and learning
• Decision-making under ambiguity
• For each: the question, source, and which story from your projects maps best

Role-Specific Questions:

• Tied directly to JD requirements
• "Tell me about a time you [specific JD requirement]"
• For each: the question, why they're likely asking it (what JD requirement it maps to), and the candidate's best angle

Red-Flag Questions:

• Questions about gaps in your profile
• "Why are you leaving your current role?"
• "Why should we hire you over someone with more experience?"
• Read the user's profile to identify what might raise questions. For each: the likely question, why it comes up, and a recommended framing (honest, specific, forward-looking — never defensive)

Step 5: STAR+R Story Mapping

For each likely question, map a story from the user's experience:

| # | Question | Best Story | Fit | S | T | A | R | Reflection | | --- | -------- | ------------- | ------------------- | --- | --- | --- | --- | ---------- | | 1 | ... | [Story Title] | strong/partial/none | ... | ... | ... | ... | ... |

Fit ratings:

• strong — story directly answers the question
• partial — story is adjacent, needs reframing
• none — no existing story (flag as gap)

Fetch detailed project context from Notion for rich STAR stories:

python3 scripts/notion/page_reader.py roma          # Agent architecture, SOTA results
python3 scripts/notion/page_reader.py sera          # Scaling agents, systematic experiments
python3 scripts/notion/page_reader.py deep-research # Multi-agent systems, evaluation
python3 scripts/notion/page_reader.py txt2sql       # Knowledge graphs, team leadership, patent
python3 scripts/notion/page_reader.py mroma         # Cost optimization, multimodal design

Reflection is critical. It separates senior from junior candidates:

• What did you learn?
• What would you do differently?
• How did this change your approach to similar problems?

Step 6: Gap Analysis

For questions where the user has NO matching story (fit = none):

1. Flag the gap explicitly
2. Suggest the closest adjacent experience
3. Propose a bridging narrative ("I haven't done X specifically, but in project Y I faced a similar challenge where...")
4. If the gap is a hard blocker, flag it as a risk area

For each gap, suggest: "You need a story about {topic}. Consider: {specific experience from profile that could become a STAR+R story}."

Step 7: Technical Prep Checklist

Based on what the company actually tests, not generic advice. Create a prioritized checklist. Max 10 items:

| # | Topic | Priority | Why (evidence) | Resources | | --- | ------- | --------------- | ----------------------------------------- | --------- | | 1 | (topic) | High/Medium/Low | (reported in interviews / required in JD) | (links) |

Prioritize by: frequency in reported interviews > JD requirements > general best practices.

Step 8: Company Signals

Values they screen for (with sources):

• What does their careers page emphasize?
• What do interviewers mention in Glassdoor reviews?
• Example: "Stripe screens for 'increase the GDP of the internet'"

Vocabulary to use:

• Terms from their engineering blog, product docs, JD
• Company-specific jargon (product names, internal frameworks)
• Example: "Anthropic says 'safety' not 'alignment'"

Things to avoid:

• Anti-patterns mentioned in negative reviews
• Topics that are sensitive for the company (recent incidents, controversies)

Smart questions to ask them:

• 3-5 questions that demonstrate research and genuine interest
• At least 1 about team/org structure
• At least 1 about technical challenges
• Never ask things easily found on the website
• Tie to recent news or blog posts discovered in Step 2

Step 9: Output

Write the complete prep guide with this header and structure:

# Interview Intel: {Company} — {Role}

**Evaluation Report:** {link to Notion evaluation report if exists, or "N/A"}
**Researched:** {YYYY-MM-DD}
**Sources:** {N} Glassdoor reviews, {N} Blind posts, {N} other

## Process Overview

(table from Step 2)

## Round-by-Round Breakdown

(per round from Step 3)

## Likely Questions

(4 categories from Step 4)

## Story Bank (STAR+R)

(mapped stories table from Step 5)

## Technical Prep Checklist

(prioritized list from Step 7)

## Company Signals

(values, vocabulary, questions to ask from Step 8)

## Gaps & Risk Areas

(flagged gaps with bridging strategies from Step 6)

Step 10: Post-Research Actions

After delivering the report:

1. Draft gap stories: Ask the user if they want to draft STAR+R stories for any gaps found in Step 6
2. Interview timeline: If the user has a scheduled interview date, note days remaining and suggest a review schedule
3. Deepen research: If company research in Step 2 was thin, suggest running the market-research skill for deeper company intel (strategy, culture, competitive landscape)

Related Skills

• job-eval — Evaluate the offer before prepping (provides archetype + gap analysis)
• market-research — Deep company research if interview intel is sparse
• job-cv-tailor — Generate a tailored CV for this specific role
• job-apply — Fill the application form