Use when displaying tabular data — to make rows scannable, support sorting and filtering, handle bulk actions, and adapt to mobile breakpoints without destroying the data structure.
Skills in this repository
jeffreytse/grimoire-core - Page 22
SkillsMP has collected 1,313 skills from jeffreytse/grimoire-core. Open a skill to review its source and details.
jeffreytse/grimoire-coreShowing 40 of 1,313 collected skills.
Use when designing screens that appear before content exists — first use, no-results, user-cleared state, or error — to orient users and guide the next action instead of showing a blank screen.
Use when designing system responses to user actions — form submission, data save, deletion, errors, and confirmations — to tell users what happened, what went wrong, and what to do next.
Use when organizing content, navigation, or features in a product so users can find what they need without confusion
Use when a UI action triggers a wait of 0.1 seconds or more — to communicate system progress, prevent duplicate submissions, and maintain perceived performance.
Use when choosing or designing primary navigation for a website or app — to select the right structural pattern (top nav, side nav, tabs, bottom nav) for the content depth and device context, and avoid the anti-patterns that account for the majority of…
Use when designing a search feature for a website or application — to structure the input, autocomplete, results list, and zero/no-results states so users find what they need with minimal friction.
Use when designing any screen layout — to arrange elements so the eye naturally travels from most to least important, reducing the cognitive effort required to parse the page.
Use when validating a UI design, prototype, or live product with real users to find usability problems before or after launch
Use when starting a design project, commissioning design work, or aligning stakeholders before any visual design begins
Use when writing button labels, error messages, placeholder text, helper text, empty state copy, and tooltip content — to make UI text specific, actionable, and honest.
Use when building or reviewing a web page's HTML structure — to ensure heading hierarchy, landmark regions, and semantic elements convey the correct document outline to screen readers and assistive technology.
Use when designing formative assessment strategies to provide ongoing feedback and guide learning during instruction
Use when creating assessment rubrics for evaluating student work, projects, or performance with clear, objective criteria
Use when designing study strategies or instructional activities that use testing and recall to strengthen long-term memory
Use when designing a learning or study system that schedules review sessions to maximize long-term retention with minimum time investment
Use when defining or structuring the skills, knowledge, and behaviors required for a role, profession, or learning pathway
Use when designing a course, lesson, module, or training program and need to define what learners will be able to do after instruction
Use when writing learning objectives for a course, lesson, or training program that are measurable and aligned to instructional activities
Use when designing a curriculum, flashcard deck, or study system intended to maximize long-term retention of facts, concepts, or procedures
Use when designing an assessment rubric for a complex task, project, or performance where multiple criteria must be evaluated consistently
Use when adding gamification mechanics to a learning, product, or engagement system to increase motivation, participation, and completion
Use when designing a short, focused learning module (3–10 minutes) targeting a single skill or knowledge application
Use when designing an online course structure including learning objectives, module sequencing, assessment, and delivery format
Use when taking notes during a lecture, seminar, class, or while reading a dense text — to capture information in a structured three-section format that builds in review, retrieval practice, and summarization without extra study sessions.
Use when learning a new concept, diagnosing why understanding feels shallow, or preparing to explain complex material — to verify genuine comprehension by explaining the topic in plain language, identify exactly where the explanation breaks down, and fill…
Use when memorizing large ordered sequences — speech outlines, vocabulary lists, historical dates, names, numbers, or any information that must be recalled in sequence without notes.
Use when brainstorming, exploring relationships between concepts, planning a project scope, or creating an overview of a topic — to generate and organize ideas visually as a radial network rather than a linear list.
Use when reading a textbook chapter, research paper, technical document, or any dense non-fiction text — to maximize comprehension and retention on first pass by converting passive reading into active questioning and retrieval.
Use when facilitating discussion, coaching, or teaching that develops critical thinking through structured questions rather than direct instruction
Use when designing classroom or online sessions that replace passive lecturing with activities requiring learner mental engagement and practice
Use when designing in-lesson or mid-unit checks that help teachers adjust instruction and help learners self-correct before a summative assessment
Use when redesigning a course so that content delivery happens outside class (video, readings) and class time is used for practice, discussion, and application
Use when facilitating a structured session where learners teach, review, or problem-solve together in pairs or small groups to deepen understanding and develop metacognitive skills
Use when facilitating a learning retrospective after a course, project, or experience to help participants extract and apply lessons
Use when rendering LLM-generated text in a browser, executing LLM-generated code, or inserting LLM output into a database or downstream system — any place LLM output leaves the AI layer and enters another execution context.
Use when building applications that make LLM API calls, run inference locally, or build agentic systems — to prevent runaway costs, infinite loops, and denial of service via unrestricted model consumption.
Use when deploying an LLM application that may have been trained on or given access to sensitive data — to assess the risk of the model leaking PII, proprietary information, or confidential context through its outputs.
Use when evaluating, auditing, or improving the quality, factual accuracy, or safety of LLM-generated outputs
Use when deploying ML models that make decisions affecting people (hiring, lending, healthcare, content moderation) or when required by regulation to assess algorithmic bias