ContentComposable · Yields to: Process, Voice, Density

Learn

Structured study plans, topic guides, exam prediction, and active recall techniques.

When to Use

• User says "teach me X", "how do I learn Y", "study plan for Z"
• User invokes /learn
• Preparing for an exam or interview

Triggers

/learn [topic]
"teach me X", "how do I learn Y", "study plan for Z",
"prepare for exam", "learning path"

Examples

Example 1

Generate a study plan

Create a structured learning path for a complex topic.

/learn distributed systems for ML training

The agent generates:
- Week 1-2: Fundamentals (CAP theorem, consistency models,
  consensus protocols)
- Week 3-4: ML-specific (data parallelism, model parallelism,
  pipeline parallelism)
- Week 5-6: TPU/GPU specifics (SPMD, FSDPv2, Megatron-LM)
- Week 7-8: Hands-on (set up distributed training, debug
  common issues)
- Daily active recall questions
- Practice exercises with increasing difficulty

Example 2

Research-backed learning

Research the best resources before creating the plan.

/researcher + /learn

Researcher finds the best current resources for learning
distributed ML: papers, courses, blog posts, GitHub repos.
Learn structures them into a progressive curriculum with
spaced repetition and active recall built in.

Example 3

Exam preparation

Prepare for a specific exam with targeted study.

/learn prepare for ML systems design interview

The agent creates:
- Common question patterns (design a recommendation system,
  design a real-time fraud detector)
- Framework for answering (requirements → architecture →
  training → serving → monitoring)
- Practice problems with sample answers
- Key concepts to have ready (embedding tables, feature
  stores, model serving, A/B testing)