# Universal Viva Oral Exam Prep > A Claude skill from pinakdhabu/Exam-prompt. - **Type:** Skill - **Install:** `agentstack add skill-pinakdhabu-exam-prompt-viva-oral-exam-prep` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [pinakdhabu](https://agentstack.voostack.com/s/pinakdhabu) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [pinakdhabu](https://github.com/pinakdhabu) - **Source:** https://github.com/pinakdhabu/Exam-prompt/tree/main/skills/viva-oral-exam-prep - **Website:** https://pinakdhabu.github.io/Exam-prompt/ ## Install ```sh agentstack add skill-pinakdhabu-exam-prompt-viva-oral-exam-prep ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Universal Viva / Oral Exam Preparation ## Overview Prepares students comprehensively for oral examination formats. Simulates examiner behavior, anticipates follow-up questions based on answer content, and provides answer framing strategies calibrated to the examiner's expected standards. Covers all viva formats from undergraduate lab viva to PhD defense. ## How This Skill Works 1. **User provides**: Subject/topic, viva type (lab, project, course, comprehensive, PhD defense), university, exam duration, panel size 2. **System analyzes**: Expected question domains, common examiner strategies, weak spots typically probed 3. **System simulates**: Mock viva session with questions, answers, follow-ups, and examiner feedback 4. **System provides**: Question bank, answer templates, confidence-building strategies ## 1. Viva Types | Viva Type | Duration | Panel Size | Question Style | Preparation Focus | | ----------------------- | ---------- | ------------------------ | -------------------------------------------------- | -------------------------------------------------------- | | **Lab Viva** | 5-10 min | 1 examiner | Procedure, results, observations, apparatus | Practical steps, expected outputs, troubleshooting | | **Course Viva** | 10-20 min | 1-2 examiners | All course topics, depth varies | Syllabus coverage, weak area probing | | **Project Viva** | 15-30 min | 2-3 examiners | Your specific project, methodology, decisions | Justify choices, explain design, results analysis | | **Comprehensive** | 30-60 min | 3-5 examiners | Multi-subject, cross-domain connections | Integrate knowledge across courses | | **PhD Defense** | 60-120 min | 3-5 examiners + audience | Deep research questions, contribution, future work | Thesis defense, contribution defense, publication record | | **Internship Viva** | 10-20 min | 1-2 panelists | Work done, skills applied, learning outcomes | Project contributions, technical skills, soft skills | | **Admission Interview** | 15-30 min | 1-3 panelists | Motivation, background, subject interest | Personal narrative, subject passion, career goals | | **Online Viva** | Variable | Screen-share + panel | Similar to above but with tech demo component | Clear speech, screen readiness, backup plans | ## 2. Examiner Strategy Profiles Examiners typically follow one or more of these strategies: | Examiner Type | Behavior | How to Handle | | -------------------------- | ---------------------------------------- | --------------------------------------------------------------------- | | **The Foundation Prober** | Starts easy, keeps asking "why" | Build answers from basics; never skip fundamentals | | **The Application-Seeker** | Always asks "give an example" | Prepare examples for every concept | | **The Devil's Advocate** | Challenges every answer | Stay calm; acknowledge then defend with evidence | | **The Comparer** | "How is X different from Y?" | Prepare compare-contrast pairs for key concepts | | **The Deep Diver** | Picks one topic and goes deep | Choose what you say carefully; every point can be explored | | **The Story Teller** | Asks about your journey | Prepare your narrative arc clearly | | **The Silent One** | Says little, expects you to keep talking | Structure answers as complete explanations; watch for non-verbal cues | ## 3. Answer Framing Framework ### Structure: CLAIM EVIDENCE LINK | Component | Purpose | Example | | ------------ | ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- | | **Claim** | Direct answer to the question | "TCP is a connection-oriented protocol." | | **Evidence** | Support with facts, data, or examples | "It uses a three-way handshake (SYN, SYN-ACK, ACK) before data transfer and includes flow control via sliding window." | | **Link** | Connect back to the broader context | "This reliability makes TCP suitable for applications like web browsing and file transfer where data integrity is critical." | ### Answer Depth Levels | Level | Depth | When to Use | | ------------ | ------------------------------------------------------ | ----------------------------------------------------- | | **Basic** | 2-3 sentences, direct answer | Initial answer; let examiner decide if they want more | | **Standard** | 4-6 sentences, with example | Most questions; shows competence | | **Detailed** | 7-10 sentences, with mechanism + example + edge case | When examiner nods or says "tell me more" | | **Expert** | Full explanation with nuance, exceptions, and research | PhD defense, comprehensive viva | ### If You Don't Know ``` Step 1: Acknowledge honestly "I am not entirely certain about [specific aspect], but I can explain what I know about [related concept]." Step 2: Show reasoning ability "Based on my understanding of the underlying principles, I believe the answer would be..." Step 3: Offer to explore "I would be happy to work through the reasoning or look into this further." Avoid: Bluffing, guessing randomly, saying "I don't know" and stopping. ``` ## 4. Common Follow-Up Probes | Initial Answer | Likely Follow-Up | Preparation | | --------------------------------- | ----------------------------------------------------------------------- | ----------------------------------------- | | "I used X algorithm." | "Why X over Y? What are the trade-offs?" | Know alternatives and their pros/cons | | "The result was Z." | "How did you validate that result? Could there be another explanation?" | Know your validation methodology | | "X is important because..." | "Under what conditions would X NOT be important?" | Understand edge cases and limitations | | "The concept works like this..." | "How does it differ in real-world vs ideal conditions?" | Know practical vs theoretical differences | | "I studied this from [textbook]." | "What are the criticisms or limitations of that approach?" | Know multiple perspectives | ## 5. Mock Viva Simulation **User**: Simulate a 10-minute lab viva on Sorting Algorithms for VTU CSE, 3rd semester. ``` Examiner: Explain the working of Quick Sort. You: "Quick Sort is a divide-and-conquer algorithm. It selects a pivot element, partitions the array around the pivot such that elements smaller than the pivot go left and larger go right, then recursively sorts the sub-arrays." Examiner: How do you choose the pivot? Does it matter? You: "Yes, pivot choice significantly affects performance. Common strategies: first element, last element, median-of-three, or random. A bad pivot (always smallest or largest) gives O(n^2) worst case. Random pivot selection probabilistically avoids worst-case behavior." Examiner: Can you implement the partition function? You: [Write pseudo-code for Lomuto or Hoare partition while explaining each step] Examiner: When would you NOT use Quick Sort? You: "Quick Sort is not suitable when: (1) stable sorting is required (it is not stable), (2) data is nearly sorted (unless randomized), (3) worst-case guarantees are critical (use Merge Sort or Heap Sort instead), (4) recursion depth is limited (embedded systems)." Examiner: Good. Compare Quick Sort and Merge Sort. ... ``` ## 6. Preparation Checklist | Task | When | Duration | | ---------------------------------------- | ------------- | -------- | | List all possible question domains | 1 week before | 1 hr | | Prepare 2-minute answers for each domain | 1 week before | 2 hrs | | Identify compare-contrast pairs | 3 days before | 1 hr | | List potential "I don't know" scenarios | 3 days before | 30 min | | Mock session with peer (record it) | 2 days before | 1 hr | | Review your own mock session | 1 day before | 30 min | | Prepare tech setup (online viva) | 1 day before | 30 min | ## 7. Common Mistakes | Mistake | Why It Hurts | Better Approach | | ---------------------------------- | ----------------------------- | ------------------------------------------------------------- | | One-word answers | Shows no depth | Expand with 2-3 sentence CLAIM EVIDENCE LINK | | Talking too long without structure | Examiner loses interest | Structured "topic sentence + explanation + wrap-up" | | Arguing with examiner | Creates negative impression | Acknowledge their point, then provide your evidence | | Not making eye contact (in-person) | Signals lack of confidence | Maintain natural eye contact; look at different panel members | | Reading from notes (online) | Noticeably reduces engagement | Use brief bullet points only; speak naturally | | Stopping at "I don't know" | Shows no problem-solving | Use "I'm not sure, but here's how I would approach it" | ## Session Config This skill integrates with the session config system (`deps/session-profile.json`). Before executing, check for an existing session profile: - If `deps/session-profile.json` exists, read `university`, `subject`, `pattern`, and `exam_type` fields to auto-configure the skill. - If the file does not exist, fall back to user-provided context or prompt the user to run `setup-exam-prompt` (or `npm run init`) first. - Session config eliminates redundant context detection — detection happens once and is reused across all skill calls. --- ## 8. Integration with Other Skills - **universal-notes-generator**: Creates structured notes that serve as answer reference material - **universal-mcq-practice-generator**: Tests fundamental knowledge that viva questions build upon - **universal-a-plus-answer-writer**: Provides structured answer templates adaptable to oral delivery - **universal-flashcard-generator**: Creates quick-recall flashcards for viva preparation - **universal-thesis-generator**: Provides thesis content for PhD defense and comprehensive viva preparation --- ## 9. STAR / PAR Framework for Behavioral & Project Vivas For project vivas, internship vivas, and behavioral questions, use the STAR or PAR framework instead of CLAIM EVIDENCE LINK. ### STAR Framework (Situation, Task, Action, Result) | Component | Purpose | Example (Project Viva) | | ------------- | ---------------------------------- | ------------------------------------------------------------------------------------------------ | | **Situation** | Describe the context | "Our final-year project was to build a real-time traffic management system." | | **Task** | State your specific responsibility | "I was responsible for the vehicle detection module using YOLOv5." | | **Action** | Explain what you did and how | "I collected 5000 annotated images, fine-tuned the model, and achieved 92% mAP on the test set." | | **Result** | State the outcome and impact | "The system reduced average intersection wait time by 34% in simulation." | ### PAR Framework (Problem, Action, Result) — Compact version | Component | Purpose | Example | | ----------- | ------------------------- | ------------------------------------------------------------------- | | **Problem** | What was the challenge? | "The existing system had 40% false positives in vehicle detection." | | **Action** | What did you do about it? | "I implemented background subtraction with adaptive thresholding." | | **Result** | What was the improvement? | "False positives dropped to 8%, and detection speed improved 2x." | ### When to Use Which | Viva Type | Recommended Framework | Why | | ----------------------------- | --------------------- | --------------------------------------- | | Technical project viva | STAR | Depth of technical contribution matters | | Internship viva | PAR | Impact-focused, time-constrained | | Behavioral / HR-style | STAR | Shows soft skills and problem-solving | | Comprehensive (cross-subject) | CLAIM EVIDENCE LINK | Knowledge-based, not project-based | | PhD defense (contributions) | STAR | Highlights specific research impact | --- ## 10. Technical Whiteboard Protocol For vivas that involve whiteboard coding, diagramming, or algorithm explanation: ### Whiteboard Communication Structure ``` Step 1: CLARIFY "Let me make sure I understand the problem. We need to..." [Paraphrase question, confirm with examiner] Step 2: THINK ALOUD "I'm thinking about this approach..." [Narrate your thought process — examiners want to see reasoning, not just the answer] Step 3: SKETCH "Let me draw the high-level structure first." [Draw boxes/lines; label components; keep it simple] Step 4: EXPLAIN "This component handles X, this one handles Y..." [Walk through the diagram; point as you explain] Step 5: CODE / DETAIL "Now I'll implement the core logic..." [Write pseudo-code or actual code left-to-right, top-to-bottom] Step 6: TRACE "Let me trace through an example to verify..." [Run a sample input through your code/diagram verbally] Step 7: WRAP "To summarize, this solution achieves..." [Recap complexity, trade-offs, edge cases] ``` ### Whiteboard Etiquette | Do | Don't | | ----------------------------------------- | ----------------------------------------------- | | Write legibly and large (visible from 2m) | Cram everything into a corner | | Use clear labels and arrows | Draw messy or unlabeled diagrams | | Keep diagrams simple (max 5-7 components) | Overcomplicate with unnecessary detail | | Erase only when space runs out | Erase immediately — examiner may refer back | | Use color if markers available | Spend time on artistic quality | | Number steps or components | Write in cursive (harder to read from distance) | ### Common Whiteboard Scenarios | Scenario | Prepar … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [pinakdhabu](https://github.com/pinakdhabu) - **Source:** [pinakdhabu/Exam-prompt](https://github.com/pinakdhabu/Exam-prompt) - **License:** MIT - **Homepage:** https://pinakdhabu.github.io/Exam-prompt/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-pinakdhabu-exam-prompt-viva-oral-exam-prep - Seller: https://agentstack.voostack.com/s/pinakdhabu - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.