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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.
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.
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.
Documents a legacy workflow for extracting reusable solutions from Claude Code sessions and saving them as learned skills. Useful for reviewing existing setups and extraction settings; this version is deprecated in favor of continuous-learning-v2.
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.
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.
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.
[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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Extract reusable solutions and project conventions from completed Claude Code sessions and save them as learned skills. This deprecated version documents the older session-end workflow and explains how it differs from its successor.
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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Learn a new skill or concept through short, interactive lessons tailored to your goals and previous learning. The skill creates HTML lessons, reference sheets, and learning records to support practice and review across multiple sessions.
Check learning-log timestamps, free disk space, and shortcut-related phrases before Claude Code ends a session. A Stop hook, which runs when the agent tries to finish, warns or blocks completion according to configured rules without using AI judgment.
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.
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.
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.
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.
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.
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.
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.
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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