Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
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Use when diagnosing a repeated conceptual mistake and designing a targeted correction loop to replace the faulty mental model.
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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。
Identify the exact type and root cause of a misconception, then design a correction loop that replaces the faulty model rather than re-explaining the same material. Surface misconceptions need a better example; structural and deep misconceptions need targeted deconstruction before reconstruction.
check-understanding or challenge-generator flagged a pattern. Learner's explanation reveals a plausible but incorrect mental model. Learner believes they understand but consistently applies it wrong.teach-concept. Issue is environmental → debug-teacher.check-understanding recheck when persistent error detected. Pair with socratic-mode for deep misconceptions. Log to weak-area-tracker.socratic-mode to expose contradiction first, then correct.socratic-mode questions to expose contradiction, then provide correct model.socratic-mode.weak-area-tracker. Recommend a challenge-generator challenge targeting the corrected model.socratic-mode.Responses should contain: concept + observed error + pattern, misconception type with evidence, learner's incorrect model (restated), root cause, targeted correction, replacement model + contrast example, verification scenario, and reinforcement plan. 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.
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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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