Custom Study Plan Generator

Create personalized study plans that adapt to the user's goals, experience, and available time.

Instructions

1. Parse the goal and constraints from: {args}
2. If the argument is "assess" or unclear, start by asking the user:
   • What are your goals? (interview prep, language mastery, CS fundamentals, career growth)
   • What's your current level? (beginner, intermediate, advanced in each area)
   • How many hours per week can you dedicate?
   • Which languages do you want to practice in?
   • Any specific companies you're targeting? (reference content/courses/interview-preparation/ guides)
   • Timeline? (weeks until deadline)
3. Read the relevant curriculum READMEs to understand available content:
   • README.md — overall structure
   • content/courses/README.md — existing structured plans
   • content/courses/programming-and-algorithms/README.md — languages, algorithms, and practice
   • content/courses/mathematics/README.md — math foundations
   • content/courses/ai-engineering/README.md — model building and AI applications
   • content/courses/software-engineering/README.md — engineering practices
   • content/courses/systems/README.md — systems design and operation
   • content/courses/data-engineering/README.md — data lifecycle
   • content/courses/interview-preparation/README.md — company guides
4. Generate a week-by-week plan

Plan Types

interview-prep [weeks] [languages...]

Optimized for technical interviews:

• Week 1-2: Core algorithms (arrays, trees, graphs, DP) in primary language
• Week 3: System design + company-specific patterns
• Week 4+: Mock interviews, weak-area drilling, secondary language practice
• Daily: 1 algorithm problem + 1 language-specific exercise from practice/
• Include company-specific guidance from content/courses/interview-preparation/{company}/

language-mastery [languages...]

Deep fluency in target languages:

• Phase 1: Language essentials (read the language chapter in content/courses/programming-and-algorithms/, work through the solutions to exercises 01-03)
• Phase 2: Intermediate patterns (exercises 04-06, language idiom focus)
• Phase 3: Advanced topics (exercises 07-10, concurrency, metaprogramming)
• Cross-language comparison: implement the same algorithm in all target languages
• Daily: 1 exercise + compare with previous language implementation

systems-mastery [languages...]

Systems programming depth:

• Phase 1: Memory management fundamentals (C exercises, then Rust comparison)
• Phase 2: Concurrency (pthreads → channels → async, across C/Rust/Zig/Go)
• Phase 3: Performance (profiling, cache-awareness, SIMD, zero-copy)
• Phase 4: Systems projects (allocator, network server, file system)
• Pair with the C runtime chapters in content/courses/ai-engineering/22-kernels/ (rt.02 arena, rt.03 thread pool)

full-stack [weeks]

Comprehensive curriculum:

• Follow the Combined Path from content/courses/README.md
• Add polyglot practice exercises alongside each algorithm topic
• Integrate company interview prep in final weeks
• Adjust pace based on user's starting level

fundamentals [weeks]

CS foundations for career changers or refreshers:

• Math foundations (prioritize discrete math + probability)
• Core algorithms (focus on patterns, not memorization)
• One systems language (C or Rust) + one general purpose (Python or TypeScript)
• Build up to practice exercises progressively

Plan Format

Generate plans as a structured weekly schedule:

## Week N: {Theme}

### Goals
- {specific, measurable goals for the week}

### Daily Schedule ({X} hrs/day)

| Day | Morning ({Y} min) | Afternoon ({Z} min) | Evening ({W} min) |
|-----|-------------------|---------------------|-------------------|
| Mon | {activity} | {activity} | {activity} |
| ... | ... | ... | ... |

### Exercises
- [ ] {specific exercise with link to file/README}
- [ ] {language practice with link to practice/build/{tier}/{lang}/}

### Milestones
- By end of week: {what they should be able to do}
- Self-check: {how to verify understanding}

Adaptation Rules

• If user knows algorithms well → skip to language practice + systems
• If user is new to a language → start with exercises 01-03 before anything complex
• If interview timeline < 4 weeks → focus on high-frequency patterns only
• If user targets quant firms → emphasize math, probability, C++ from practice/
• If user targets AI labs → emphasize ML track + Python + systems
• If user targets cloud companies → emphasize Go, Java, system design
• Always pair theory with implementation: every concept gets coded
• Schedule spaced review: revisit topics from 3 days ago, 1 week ago, 2 weeks ago
• Include use of study-session skill for active recall checkpoints

Cross-References

Reference specific content from the curriculum:

• Algorithm patterns → content/courses/programming-and-algorithms/{topic}/README.md
• Practice exercises → the language chapter in content/courses/programming-and-algorithms/
• Interview prep → content/courses/interview-preparation/{company}/README.md
• Study routines → content/courses/README.md (daily routine sections)
• Math prerequisites → content/courses/mathematics/README.md
• Competitive programming → content/courses/programming-and-algorithms/README.md
• Worked builds → content/courses/case-studies/{study-dir}/README.md (each has a runnable implementation)