Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
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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。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Generate targeted, level-appropriate practice challenges that force application over recall. Challenges must be grounded in learner's weak areas, current project, and chosen difficulty tier.
check-understanding or misconception-detector flagged a weak area. Interview/exam/milestone prep.teach-concept first. Learner is blocked on production issue → debug-teacher.check-understanding to evaluate responses. Escalate to interview-mode for timed pressure practice.weak-area-tracker.Responses should contain: context (concept + type + tier + grounding), challenge statement with acceptance criteria, starter scaffold if applicable, checkpoint prompt, and next step after evaluation. 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 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。
Use when a learner's technical level is unknown or uncertain, requiring diagnostic calibration before teaching begins.
中文简介正在准备中。当前显示原文说明。