studykit-manager

name	studykit-manager
description	Central dashboard for coding interview practice. Shows unified progress view, due notes, auto-orchestrates study plan based on performance, and provides proactive suggestions.

Studykit Manager

Interview prep coach. Tracks progress, recommends next steps, adapts to your target company and role.

Arguments

Parse $ARGUMENTS for:

• No arguments or --status: Show dashboard
• --target [company] [role]: Set job target and customize plan
• --add [url or topic]: Add item to study plan with source
• --suggest: Get recommendations based on your situation
• --sync: Update plan based on performance

---

Data Sources

Read these files (skip if missing):

1. .interview/user-profile.md — Target level, language, companies
2. .interview/system-design-plan.md — Study items with checkboxes
3. .interview/performance/performance_summary.md — Stats, patterns, scores
4. .interview/notes/*.md — Learning notes

---

--target [company] [role]

Set the job context and customize the plan.

Example: /studykit --target Snowflake "Senior ML Engineer"

Actions:

1. Research the company using web search:

   • What does [company] ask in system design interviews?
   • [company] engineering blog
   • [company] tech stack

2. Update user-profile.md with:

   ## Current Target
   Company: [company]
   Role: [role]
   Updated: [date]
   

3. Prioritize relevant items in system-design-plan.md:

   • Move company-relevant problems to top
   • Add company-specific problems if missing
   • Note source: "Prioritized for [company] [role]"

4. Display customized recommendations:

   =================================================================
   TARGET SET: [Company] - [Role]
   =================================================================
   
   Based on [company]'s tech stack and interview patterns:
   
   HIGH PRIORITY (company-specific):
   - [Item] — [why it's relevant]
   - [Item] — [why it's relevant]
   
   RECOMMENDED READING:
   - [Company engineering blog post]
   - [Relevant DDIA chapter]
   
   LIKELY INTERVIEW TOPICS:
   - [Topic based on research]
   
   =================================================================
   

---

--add [url or topic]

Add an item to study plan with source tracking.

If URL provided:

1. Fetch and extract key topics
2. Add as checklist items with source link
3. Mark as user-provided (higher priority)

If topic provided:

1. Add as checklist item
2. Ask for source if not provided

Format in plan:

- [ ] [Topic] — *Source: [url or "user"]*

Prioritization:

• User-provided sources rank higher than auto-generated
• Company-relevant items rank higher than general
• Items from engineering blogs of target company rank highest

---

--suggest

Give situational recommendations. Be a coach, not a list generator.

Consider:

1. Target company — What do they ask? What's their stack?
2. User's weak areas — Based on performance data
3. Time to interview — If known, prioritize accordingly
4. Recent activity — What have they been studying?

Output style:

Based on your target ([company] [role]) and your progress:

You're strong at: [patterns]
You need work on: [patterns]

My recommendation:
[Specific, actionable advice - not a generic list]

Next step: [Single clear action]

Example:

Based on your target (Snowflake Senior ML Engineer) and progress:

You're strong at: Arrays, Two Pointers
You need work on: System Design (0 problems done)

Snowflake's interviews focus heavily on data infrastructure.
They'll likely ask about: distributed storage, query optimization,
data pipelines at scale.

My recommendation:
Start with "Design a Data Warehouse" - it's directly relevant to
Snowflake's core product. Then read their engineering blog post
on how they handle petabyte-scale queries.

Next step: Design a Data Warehouse problem

---

Dashboard (default)

=================================================================
STUDYKIT
=================================================================

Target: [Company] - [Role] (or "Not set")

Stats: [N] sessions | [N]% pass rate | [N]/5 avg

---

NEXT UP:
[Single most important thing to do right now with reasoning]

---

PROGRESS:
Coding: [N]/[M] patterns practiced
System Design: [N]/[M] problems done
Notes: [N] total, [N] due for review

---

Commands:
/studykit --target [company] [role]
/studykit --add [topic or url]
/studykit --suggest
=================================================================

---

--sync

Update plan based on performance.

1. Mark mastered patterns (90%+ pass rate, 4.5+ avg)
2. Flag struggling areas (< 60% pass rate)
3. Reorder items based on current target company
4. Output what changed

---

Guidelines

• Be a coach, not a bureaucrat — Give opinions, not just data
• Context matters — Recommendations depend on target company/role
• Sources tracked — Every item has a source; user-provided ranks higher
• No fluff — Direct, actionable, specific
• One next step — Always end with a single clear action