Education Agent Skills plugin for Grok Build

Skills (25)

• adaptive-hint-sequence-designer — Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.
• ai-facilitated-collaborative-learning-designer — Design AI-supported collaborative tasks that structure group interaction and address participation problems. Use when students struggle to collaborate effectively on group tasks.
• ai-feedback-design-principles — Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.
• cognitive-tutoring-architecture-designer — Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.
• digital-worked-example-sequence — Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.
• erroneous-example-designer — Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.
• formative-assessment-loop-designer — Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.
• individual-spacing-algorithm-explainer — Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.
• intelligent-tutoring-dialogue-designer — Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.
• learning-analytics-interpretation-guide — Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
• metacognitive-monitoring-ai-contexts — Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
• productive-failure-desirable-difficulty-designer — Redesign a direct instruction sequence to include productive struggle before the explanation phase. Use when teaching concepts that benefit from failure-first approaches.
• self-explanation-prompt-designer — Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
• worked-example-to-problem-solving-transition-designer — Design the transition from worked examples to independent problem-solving using expertise-reversal principles. Use when students follow examples but cannot solve problems alone.
• ai-expertise-interrogation-designer — Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
• ai-hallucination-fact-check-protocol — Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.
• ai-learning-boundary-mapper — Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
• ai-output-critical-audit-designer — Design a structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.
• ai-socratic-dialogue-designer — Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.
• disciplinary-ai-literacy-sequence-designer — Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.
• prompt-literacy-sequence-designer — Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
• coverage-audit — Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.
• curriculum-crosswalk — This skill compares two or more band-tagged frameworks and produces a framework-neutral topic matrix as its primary output. Its input is the output of the Developmental Band Translator skill — one output per framework. Every framework is tr
• developmental-band-translator — This skill tags an external curriculum — already decomposed by the curriculum harness into KUD items, learning targets, and (optionally) criteria — with a school's developmental band labels. The school's band schema is supplied as an input;
• kud-chart-author — This skill authors or reviews Know/Understand/Do (KUD) charts for learning targets in competency-based developmental band frameworks. A KUD chart is a planning and teaching tool — not an assessment structure — that specifies what students n