explanatory-output-style
claudeAdds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Open-source education plugin with formative assessment and student feedback skills for AI agents: feedback that moves learners forward, assessment design, hinge questions, success criteria, peer and self-assessment, and responsive analysis of student work.
Plugin nurture from marketplace nurture in goNurture/agent-plugin. Commands read from the repo's manifest — 1 month ago.
analysing-student-workRead this when you have more than one learner's work in front of you and the question is what to do about it. Use it when given a class set, assignment or quiz results, a mark sheet or response CSV, or when asked what a class got wrong, which items were poor, who needs intervention, or what to teach
designing-assessmentsRead this before generating any quiz, test, task or item — assessment written item-first drifts to recall and produces a score nobody can act on. Use it when asked to build a quiz, test, exam paper, question set, retrieval practice or knowledge check, to write or fix items and distractors, or to jud
eliciting-evidenceRead this when the question is how to find out what a class is thinking during a lesson, rather than what to ask them. Use it when asked to improve questioning, check understanding mid-lesson, run a discussion, involve quieter students, or design an exit ticket or do-now — and when a teacher says th
formative-assessmentRead this when a teaching request touches assessment, feedback, or checking understanding, and you need to know which of the eight Nurture skills applies. Use it when the request is broad ("help me plan this unit", "how do I know they've got it?"), spans several skills, or when the user asks about a
giving-feedbackRead this before writing any comment, mark, or response on a piece of student work — your default behaviour here is the documented failure mode. Use it when asked to mark, comment on, respond to or improve feedback on student work, a submission, or a class set, and when designing a marking or feedba
hinge-questionsRead this whenever a teacher needs to know, mid-lesson, whether the class has got it. Use it when asked for a quick check for understanding, a diagnostic or multiple-choice question, an exit-ticket question, or when someone needs to decide between moving on and reteaching. It produces a single item
peer-and-self-assessmentRead this when learners are going to assess work — their own or each other's — because unstructured peer feedback reliably degrades into praise and personal comment. Use it when asked about peer review, peer marking, self-assessment, reflection prompts, metacognition or student ownership, and when a
success-criteriaRead this when a task needs a stated standard — feedback, peer assessment and self-assessment all fail silently without one. Use it when asked for learning objectives, lesson aims, WALT/WILF, success criteria, a rubric or mark scheme for learners, or a checklist for a task, and when feedback has now
nurture-signalconnects to: https://signal.gonurture.com/mcp
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Teaches Claude container's command surface and how it maps to Docker, Lima, Colima, and Podman on macOS
1 skill
Agent skills for Azure SQL Developer, the Azure SQL Database engine running locally in a container. Teaches your agent to run the engine, connect, migrate, scaffold, build RAG, wire CI, and go local-to-cloud, using the real Private Preview image instead of the SQL Server image.
17 skills
Teaching skill: cookbook-style walkthrough for signed audit trails on every Claude Code tool call. Cedar policy, Ed25519 receipts, offline verification, CI/CD integration, SLSA composition. Pairs with the protect-mcp runtime plugin.
1 skill
Evidence-first reading of supplied documents. Five modes (quick/deep/map/feynman/book), claim-reason-evidence decomposition, confidence labels, evidence ledger, knowledge maps, Feynman teach-back.
1 skill
Study p-hacking in econometric designs: map researcher degrees of freedom, audit a specification search with a complete ledger and null-calibrated honest p-values, replay realistic search procedures, detect p-hacking in a literature, immunise an analysis, and benchmark whether an AI agent p-hacks. Research and teaching use only.
11 skills
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