Build a grounded recommendation-system study plan, explain a topic for a graduate student or new engineer, and track local progress. Use for learning, revision, interview preparation, and deciding what to study next.
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Turn a topic or a complete repository course into an evidence-based learning loop.
python3 tools/recsys_agent.py doctor; build the ignored index if needed.course list, course start <new-engineer|graduate>, then course today --minutes N. Read docs/learning-agent-guide.md and the returned unit.learn "<topic>" --level <level> --minutes <N>; never invent page references.course submit or evidence-gated complete. Scores below 0.8 or missing practice/failure evidence remain assessed, not completed.course feedback; generate the next bounded task with course today.course review for delayed retrieval and transfer tasks, and self-eval for a local machine-readable report.State belongs only in ignored .recsys-workspace/. Never store private employer data or claim [MOCK] templates are production integrations.
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Review recommendation-system code, architecture, experiment, or design for correctness, serving safety, offline-online consistency, capacity, observability, and rollback gaps.
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Answer recommendation-system questions from the released book PDFs with chapter and page citations. Use for explanations, comparisons, formulas, architecture concepts, and questions asking what the book says.
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Diagnose recommendation quality, traffic, feature, model, retrieval, ranking, latency, timeout, overload, degradation, or logging regressions using an evidence-first decision tree.
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Design, implement, debug, and review industrial recommendation-system architecture or algorithms using the released book PDFs, curated examples, and runnable templates.
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Design, implement, debug, and review industrial recommendation-system architecture or algorithms using released PDFs, curated examples, and runnable templates.
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Run and extend the repository's recommendation-system projects covering the ranking funnel, two-tower ANN, point-in-time feature consistency, and online serving reliability.
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