Interview Prep
Start from the end: turn every learning session directly into interview readiness.
Core Files
- Knowledge Base:
references/knowledge-base.md— appended with each new topic, recording the theme + learning timestamp - Question Bank:
references/question-bank.md— all interview questions aggregated by topic for easy self-review
Input
Any learning content submitted by the user: study notes, technical concepts, project descriptions, etc.
Output: Five-Step Process
For every input, execute the following five steps:
Step 1 - Feynman Test (ELI5 + Professional)
Describe the concept in two ways:
- ELI5: As if explaining to a 10-year-old
- Professional: Complete, rigorous, no key details omitted
Purpose: Verify true understanding, not rote memorization.
Step 2 - Interview Question Generation
Generate 5-8 high-frequency interview questions in three categories:
- Fundamentals (what / differences / principles)
- Deep Dive (why / how / tradeoffs)
- Applied (examples / scenario-based)
Each question includes:
- What it tests
- Key answer points
- Follow-up direction if answered incorrectly
→ Also append to question-bank.md (aggregated by topic)
Step 3 - STAR Story Extraction
Break down the content into reusable STAR narratives:
- Situation: Background (technical scenario / business constraints)
- Task: Goal (what you needed to solve)
- Action: What you specifically did
- Result: Quantified outcomes + lessons learned
Best for: project experiences, problem-solving stories, team collaboration.
Step 4 - Analogical Learning (One to Three)
- Same-level analogy: What is this like in everyday life? What else works this way?
- Deeper analogy: What is one level below this? What's the underlying principle?
- Transfer analogy: Where else can this approach be applied?
Purpose: Build a knowledge network, not isolated facts.
Step 5 - Weakness Diagnosis + Knowledge Archive
Proactively uncover vulnerabilities:
- Where will interviewers probe until you can't answer?
- What do you think is important but actually isn't?
- What classic pitfalls remain unfilled? (edge cases, concurrency, distributed tradeoffs)
→ Append to knowledge-base.md with format:
## [Topic]
- Learned at: YYYY-MM-DD HH:mm
- Core takeaway: one-sentence summary
- Weak spots to reinforce: [spot 1, spot 2, ...]
Output Format Template
## 📚 Topic: [User's Input Topic]
---
### 1. Feynman Test
**ELI5:**
> [One-sentence version]
**Professional:**
> [Full description]
---
### 2. Interview Questions
| # | Question | Tests | Key Points |
|---|----------|-------|------------|
| Q1 | | | |
**Follow-up traps:** ...
---
### 3. STAR Story
- **S**: [Background]
- **T**: [Goal]
- **A**: [Action]
- **R**: [Result + Reflection]
---
### 4. Analogical Learning
- 🔗 **Same-level**: ...
- 🔬 **Deeper**: ...
- 🚀 **Transfer**: ...
---
### 5. Weakness Diagnosis
⚠️ Likely follow-up pressure points:
1. ...
2. ...
---
*Synced to Knowledge Base & Question Bank*
File Structure
interview-prep/
├── SKILL.md
└── references/
├── knowledge-base.md # Learning timeline
└── question-bank.md # Interview questions by topic
Trigger Words
When the user says/submits:
- "I learned XXX today"
- "Help me prepare for an interview"
- "Generate interview questions from these notes"
- "What interview questions can come from this concept"
- "What questions can this project be asked"
→ Activate this skill and run the five-step process, updating both documents.