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Define the scope and purpose of a complex inquiry before mapping or futures work begins. Use when a class or group needs to agree what they are investigating, who it matters to, and at what scale.
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Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.
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Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.
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Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
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Design a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.
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Analyse student work against criteria to identify specific gaps between current performance and learning objectives. Use when reviewing submissions, planning feedback, or diagnosing learning needs.
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Generate a criterion-referenced rubric with descriptive performance levels for a task or objective. Use for marking guides and general curriculum contexts. For Manning programmes where Competent = success, use coherent-rubric-logic-builder instead.
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Build a five-level rubric with coherent logic for a learning target within a developmental band. Use for Manning methodology programmes where Competent = success. For general curriculum rubrics, use criterion-referenced-rubric-generator instead.
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Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.
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Design a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
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Bloom's Taxonomy of Educational Objectives and mastery learning as practical instructional design tools. Covers the six cognitive levels (remember, understand, apply, analyze, evaluate, create), the revised 2001 taxonomy, the knowledge dimension, mastery learning loops, formative vs. summative assessment, and the writing of learning objectives that are both specific and verifiable. Use when designing lessons, writing objectives, building assessments, or diagnosing why a learner is stuck at one cognitive level.
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Public speaking and oral presentation skills for effective communication. Covers speech structure (introduction, body, conclusion), delivery techniques (projection, pace, pause, gesture), audience analysis, impromptu and extemporaneous methods, managing anxiety, visual aids, and persuasive versus informative speaking. Use when preparing speeches, practicing delivery, analyzing presentations, or building confidence in oral communication.
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Creative reasoning framework for ill-defined problems where conventional solutions are suboptimal. Use when the problem has ambiguous goals, vast solution space, no single correct answer, or requires innovation. Implements three paradigms — combinational (novel combinations of familiar ideas), exploratory (expand solution space boundaries), transformative (alter fundamental constraints). Do not use for well-defined problems, mathematical puzzles, or tasks requiring convergent reasoning.
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Receive and act on code review feedback with technical rigor, not performative agreement or blind implementation
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Content angle generator for turning dry expert topics into high-engagement infotainment. Applies 7 journalistic angle types (Myth vs Reality, Paradox, Level Error, Price of Solution, Conditions, Hidden Driver, Contrary Thesis) + historical/future lens + news-hook framing. Use when experts need their content to become interesting rather than just informative - blog posts, video scripts, social content, newsletters, landing pages, ad hooks. For any niche: B2B, B2C, services, products, personal brand.
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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.
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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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Design tools for AI agents — clean function interfaces, typed schemas, informative errors, idempotency, and tool documentation. Use when giving an agent new capabilities or fixing flaky tool use.
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Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.
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Use when a task needs the judgment of a Postsecondary Art, Drama, or Music Professor — diagnosing a jury/crit score that contradicts a semester of formative work, building a studio-art or performance tenure dossier around exhibitions and productions instead of publications, staffing applied one-on-one lessons or crit sections between adjuncts and full-time faculty, running a structured critique, or deciding whether a failed barrier jury means a change of major.
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Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.
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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
中文简介正在准备中。当前显示原文说明。
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
中文简介正在准备中。当前显示原文说明。