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Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
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Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
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Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.
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介绍在Claude Code会话结束时提取并保存可复用解决步骤的旧版机制。该版本已弃用，适合检查现有环境的兼容性、提取设置及已保存的学习内容。
Extract decisions, lessons, recurring patterns, and unexpected findings from the artifacts of a completed development phase. It organizes these learnings into a LEARNINGS.md file using planning, summary, verification, acceptance testing, and state documents.
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根据学习 Claude 用法或团队引入需求，查看 Claude Academy 的课程、教程和应用实例，提供符合学习目标的资料。
Compare job requirements with your current skills and experience to identify learning priorities. Get a report with ranked gaps, study resources found through web searches, estimated learning time, and a recommended study sequence.
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[omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief without popularity ranking. Use when the user says: jit-learn, learn next, learn now, blocker-specific learning target, highest-leverage learning target, immediate learning payoff, immediately applicable learning brief, source-backed learning brief.
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Manage the operational learnings lifecycle — load prior learnings to inform current work, harvest new patterns worth preserving, and keep the document tight over time. Provides a protocol for accumulating actionable patterns from practice that complement standards and defaults. Use when a workflow session completes and produced insights worth persisting, when starting a session that should benefit from prior patterns, or when the user says 'harvest learnings', 'what have we learned', 'capture this pattern', 'tighten learnings', 'compress learnings', or 'operational learnings'.
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这是在Claude Code会话结束时，从对话中提取可复用解决步骤并保存为已学习技能的旧版机制。目前已弃用，资料也提供了转向后续版本的说明。
Expert in designing engaging learning experiences - completion optimization, multimedia learning, interactivity, gamification, and retention strategies. Covers the science of how people learn and practical techniques to make courses that students actually finish. Use when "learning experience, course engagement, completion rate, make learning fun, gamification, interactive course, learning design, " mentioned.
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Expert in applying AI to education - AI tutors, personalized learning paths, content generation, automated assessments, and adaptive learning systems. Covers practical implementation of AI to enhance (not replace) human instruction. Use when "ai tutor, ai for learning, personalized learning, adaptive learning, ai assessment, generate course content, ai education, " mentioned.
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[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.
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根据学习目标和已有理解生成简短的互动课程，通过测验、实践任务与反馈进行练习，积累复习资料和学习记录，支持跨多次会话的持续学习。
在Claude Code结束工作时检查学习记录的更新时间、磁盘可用空间和会话末尾的特定表述，按配置发出警告或阻止完成，并提示可能省略步骤的迹象。
Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.
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Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.
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Build learning activities where learners explain an idea first, then improve their explanation through focused questions. Include plain language, checks on analogies, application to unfamiliar situations, and a record of remaining gaps and next goals.
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Add social-emotional learning—practicing emotional awareness, self-management, empathy, and collaboration—to subject lessons in OpenMAIC. It connects these goals to academic concepts through discussion, choices, feedback, retries, and reflection.
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This skill is identified as development related, but the supplied text gives no concrete workflow or expected result. Its name suggests continuous learning, yet the material does not explain any learning behavior or how to use it.
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Audit saved project learnings against the current codebase to find outdated explanations, overlapping documents, and contradictions. Maintain the documentation with evidence-based updates, consolidation, replacement, or deletion, and deliver a per-document report with committed changes when applicable.
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Organize medical guidelines into structured learning tracks, preview upcoming units, and manage delivery using recorded progress. It also supports investigating duplicate or missed deliveries and planning transitions to new guideline versions using authoritative sources.
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This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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