Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
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Use when creating, testing, or updating active-recall flashcards grounded in session concepts to reinforce retention.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Convert session concepts into structured active-recall flashcards. Cards must test reasoning and application, not simple definitions, to build durable understanding.
spaced-repetition needs card generation/update. Revision period (exam, interview, milestone).deep-dive. Active debugging/project work → would interrupt flow.spaced-repetition for scheduling. Cards failed 3 times → trigger misconception-detector.Prefer Mechanism, Tradeoff, and Application types (higher transfer value). At least 60% of cards should be these types.
misconception-detector.Card generation: topic, date, cards (front + back + type + difficulty). Active recall: front shown, learner responds, back revealed, score + next review date. Format naturally.
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Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
中文简介正在准备中。当前显示原文说明。
Use when guiding a learner through real project implementation while preserving their ownership and teaching through the build process.
中文简介正在准备中。当前显示原文说明。
Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.
中文简介正在准备中。当前显示原文说明。
Use when verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.
中文简介正在准备中。当前显示原文说明。
Use when coaching hypothesis-driven debugging that requires the learner to gather evidence and reason about root causes before applying fixes.
中文简介正在准备中。当前显示原文说明。
Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.
中文简介正在准备中。当前显示原文说明。