'learning' 검색 결과

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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• Train agents from historical experience
• Build decision-making for games and navigation
• Combine experience from multiple agents for training

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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• Train agents from historical experience
• Build decision-making for games and navigation
• Combine experience from multiple agents for training

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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• Creating a study plan when starting the curriculum
• Choosing a starting phase based on current knowledge
• Recording learning goals, available time, and a build goal

Claude Code 세션 종료 시 재사용할 해결 절차를 추출해 저장하는 구버전 방식을 설명합니다. 현재는 사용이 중단되어 기존 환경의 호환성과 설정을 확인하는 참고 자료로 활용합니다.

• 구버전 세션 종료 훅 설정을 확인합니다
• 추출 기준과 대상 분류를 조정합니다
• 저장된 학습 스킬을 검토합니다

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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• Reviewing decisions made during a completed phase
• Gathering lessons from planning and verification artifacts
• Recording discovered patterns and unexpected findings

Find Claude Academy learning resources that closely match questions about using Claude, its features, or team adoption. It supplements direct answers with a small selection of relevant courses, tutorials, worked examples, or product learning hubs.

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• Find resources for getting started with Claude
• Learn how to use a specific Claude feature
• Choose learning materials for team adoption

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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• Identify skill gaps across tracked jobs
• Build a learning plan for one job posting
• Choose study resources and a learning sequence

[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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Claude Code 세션 종료 시 대화에서 재사용할 해결 절차를 추출해 학습된 스킬로 저장하는 구버전입니다. 현재 사용이 권장되지 않으며 후속 버전으로의 전환 안내도 포함합니다.

• 기존 세션 종료 학습 흐름을 확인할 때
• 저장된 학습 스킬을 정리할 때
• 패턴 추출 기준을 조정할 때

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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학습 목표와 현재 이해도에 맞춰 짧은 대화형 수업을 만듭니다. 퀴즈와 피드백으로 연습하고, 복습 자료와 학습 기록을 쌓아 여러 세션에 걸친 학습을 지원합니다.

• 새로운 분야를 여러 세션에 걸쳐 학습
• 퀴즈와 피드백으로 연습
• 복습용 요약표 만들기

Claude Code가 작업을 마칠 때 학습 기록 수정 시각과 디스크 여유 공간을 확인해 조건에 따라 완료를 막습니다. 대화에서 절차 생략을 암시하는 표현도 경고합니다.

• 복잡한 편집 후 학습 기록을 확인할 때
• 디스크 공간 부족 시 완료를 막을 때
• 확인 생략을 암시하는 표현 탐지

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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• Resuming lessons from recorded progress
• Learning concepts through coding exercises
• Checking understanding with lesson quizzes

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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• Submitting training jobs and monitoring logs
• Registering data and model versions
• Connecting preprocessing and training steps

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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• Adding emotional check-ins to the start of a subject lesson
• Practicing empathy through discussion of different viewpoints
• Building feedback and retry paths into learning activities

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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• Learn a concept by explaining it in your own words
• Turn existing materials into explanation-based activities
• Test explanations without relying on technical terms

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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• Review the stated purpose of a development skill
• Check whether learning behavior is documented
• Assess available usage information before adoption

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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• Checking saved learnings against current code
• Consolidating overlapping knowledge documents
• Correcting outdated paths and links

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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• Creating a learning track from a verified medical guideline
• Previewing the next unit without changing progress
• Investigating duplicate or missed learning deliveries

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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