explanatory-output-style
claudeAdds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Study sessions where Claude asks and you answer. Finds the gaps in what you know about a topic, reviews your mind maps without redrawing them, marks brain dumps against your material, and picks the best interleaving technique for each review session.
Plugin alinear-study from marketplace alinear in SedWilliams/alinear_skills. Commands read from the repo's manifest — 12 days ago.
best-interleaving-techniquePick the best interleaving technique, or combination of techniques, for a spaced interleaved retrieval (SIR) session, and explain how to do each one properly so it exposes the most knowledge gaps. Chooses from the user's Interleaving Table (mindmap brain dumps, relational and evaluative generated qu
gap-finderRun a retrieval session that finds the gaps in the user's knowledge schema for a topic, using the question design from their Learning Science notes in Notion (iCanStudy SIR, Bloom's levels, the interleaving table, the Bear Hunter System) and the tutoring conduct of the `learn` skill. Asks one questi
mind-map-dumpCheck the accuracy of a mind map dump, a free-recall brain dump drawn as a mind map from memory, against the source material. Reports what was recalled accurately, what's wrong, what's missing, and what the learner was unsure of, layer by layer, using the rules in the user's Learning Science notes i
test-mind-mapTest the quality of a mind map, concept map, knowledge graph, or other graph-based notes. Finds the map's defects, what each one costs in recall and higher-order learning, and what in the learner's technique caused it, holding the map to the user's Learning Science notes in Notion (GRINDE, the 2-4 r
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Cross-session memory and token savings for coding agents.
6 skills · 1 agent · 1 MCP server · hooks
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
26 skills · 4 commands · 3 agents · 1 MCP server
Claude Code native multi-agent orchestration with intelligent model routing across agents and skills. Zero learning curve. Maximum power.
85 skills · 20 commands · 19 agents · 1 MCP server · hooks
Turn any learning intent into a personalized Coursera experience. Asks three quick questions (topic, familiarity, preferred format), searches Coursera's catalog, and delivers the right next step — a course, hands-on project, short video, or live roleplay — then maps a path forward. Requires the Coursera connector for catalog tools.
1 skill
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