Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
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Use when a learner needs a structured review session prioritizing high-value topics by recency, error history, and upcoming deadlines.
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Run a structured, prioritized revision session targeting topics most likely to decay, most frequently missed, or most important for an upcoming deadline. Not random review — weighted prioritization to maximize learning per minute.
weak-area-tracker shows accumulated weak areas. 3+ sessions since last review.build-with-me. Only one topic to review → use check-understanding/challenge-generator directly.weak-area-tracker. Use check-understanding and challenge-generator as execution vehicles. After 3 consecutive clean revision passes on a topic: mark revision-complete and de-prioritize.learning-memory), active weak areas, upcoming deadline + type, session time budget, self-assessed confidence per topic (optional).check-understanding or challenge-generator. Mix modes. Run 3–5 due flashcards per topic if available.misconception-detector.weak-area-tracker. Recommend next session if deadline still approaching.Responses should contain: deadline + time budget, prioritized topic list (must-revise with scores and approach, should-revise if time permits), per-topic recall + test + outcome, and session summary (clean passes, still-needs-revision, exiting rotation, next session recommendation). Format naturally.
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Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
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Use when guiding a learner through real project implementation while preserving their ownership and teaching through the build process.
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Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.
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Use when verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.
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Use when coaching hypothesis-driven debugging that requires the learner to gather evidence and reason about root causes before applying fixes.
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Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.
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