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Found 992 Skills
Find and use icons from the @clubmed/trident-icons library. Use when a user asks which icon to use for a given concept, writes <Icon name= without knowing the right name, mentions "@clubmed/trident-icons", asks "quelle icône pour X", "which icon for Y", "find me an icon", "what icon represents Z", "liste les icônes de transport", or needs import code for Trident icons. Also triggers when writing or reviewing a component that should display an icon from the Club Med design system.
Build interactive timeline components that display events and milestones sequentially. Use SfTimeline with alignment controls, custom items, and event handlers. This skill covers timeline configuration and customization for creating visual timelines in Blazor applications.
Implement Syncfusion React HeatMap Chart component for data visualization. Use this skill when user needs to create heatmaps, visualize 2D data patterns, display matrix data with color gradients, configure axes (numerical/categorical/datetime), implement legends, handle cell selection, apply custom styling, or work with large datasets. Covers installation, data binding, axis configuration, appearance customization, interaction patterns, tooltips, events, and accessibility.
Implement Syncfusion Angular HeatMap Chart component for visualizing two-dimensional data with color gradients. Use this skill whenever users need to create heatmaps, visualize data matrices, display data with color-coded cells, configure axes (numeric/categorical/datetime), add legends, customize colors and rendering modes, handle cell selection and events, or implement accessibility features. Includes data binding, axis types, interactive selection, tooltips, and bubble heatmaps.
A friendly, hand-drawn sketch interface inspired by pencil illustrations on warm cream paper. Soft teal brand accents, hand-written display headings, rounded pill controls.
Plan short-form video, vlog, travel, lifestyle, documentary, or cinematic social video scripts by negotiating duration, choosing a story arc and visual style, then splitting the film into timed scenes of 4-15 seconds. Use this skill whenever the user asks for a video plan, short video plan, scene breakdown, montage plan, travel video script, TikTok/Reels/Shorts storyboard, or cinematic short-form narrative, even if they only describe a loose theme or destination.
API reference: Core Animation (QuartzCore). Query for CALayer, CAAnimation, CABasicAnimation, CAKeyframeAnimation, CASpringAnimation, CATransaction, CAShapeLayer, CAGradientLayer, CAEmitterLayer, CATransform3D, CADisplayLink.
Technology-agnostic guidance for modular systems: bounded contexts, clear boundaries, composability, state isolation, explicit contracts, failure containment, scaffolding workflows, split/merge criteria, sub-units inside a context, and compliance review signals. Use when designing or reviewing module structure, service boundaries, package layout, cross-cutting dependencies, "how should we split this?", modularity assessments, coupling between domains, greenfield context design, or architecture discussions without assuming a specific framework, language, or repository layout. Do NOT use for executing the full Patterns 1–5 repo decomposition pipeline or per-pattern inventories (use modular-decomposition), phased extraction roadmaps as the main deliverable (use decomposition-planning-roadmap), or end-to-end legacy migration strategy (use legacy-migration-planner).
Implements and customize Syncfusion .NET MAUI DataGrid (SfDataGrid) for displaying tabular data. Use when working with MAUI data grids, SfDataGrid, tabular data display, data binding to grids, or column configuration. Covers editing cells, sorting, filtering, grouping, paging, exporting to Excel/PDF, row operations, selection, and summaries.
Complete glitch art, datamosh, and video distortion effects system. PROACTIVELY activate for: (1) Datamosh/pixel bleeding effects, (2) VHS/analog glitch simulation, (3) Digital corruption effects, (4) Displacement mapping, (5) Wave/ripple distortions, (6) Pixelation and mosaic effects, (7) Chromatic aberration, (8) Scan line effects, (9) Time-based distortions (echo, trails), (10) Lens distortion and barrel effects. Provides: minterpolate for datamosh, displacement filter, geq pixel manipulation, noise and artifacts, rgbashift/chromashift for color separation, lagfun for trails, tmix for frame blending, tblend for frame difference effects.
Deep reference for the Sui object model: ownership types, object abilities, dynamic fields, collections, versioning, transfer patterns, and derived objects. Use this skill whenever the user asks about Sui objects, object ownership (address-owned, shared, immutable, wrapped), how to transfer or share or freeze objects, dynamic fields vs dynamic object fields, Table vs Bag vs VecMap, object versioning, wrapping and unwrapping, the Receiving type, custom transfer rules, hot potato pattern, capability pattern, object deletion, Object Display, or how to model data (inventories, registries, nested items) in Sui Move. Also use when the user needs to choose between ownership types or storage patterns for their use case.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**