Interview Driven Learn

Interview-driven is all you need. Drives end-to-end tech learning with interview standards. Activated when the user submits study notes, project summaries, or technical concept explanations. Transforms any learning input into interview-ready output using a five-step process: (1) Feynman test (ELI5 + professional), (2) interview question generation with answer points and follow-up traps, (3) STAR story extraction, (4) analogical learning, (5) weakness diagnosis. Auto-maintains two reference documents: a Knowledge Base (learning timeline) and a Question Bank (all questions aggregated by topic for self-review). Designed for computer science students preparing for backend, algorithm, or system design interviews at top internet companies.

depictlightning@depictlightning

Install

openclaw skills install @depictlightning/interview-driven-learn

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:

markdown

## [Topic]
- Learned at: YYYY-MM-DD HH:mm
- Core takeaway: one-sentence summary
- Weak spots to reinforce: [spot 1, spot 2, ...]

Output Format Template