Results for “box”

Search jobs, manage applications, and run the employer job lifecycle on the WorkorAI talent marketplace with white-box candidate match explanations.

Expert knowledge for Azure Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when deploying IoT Edge modules, Kubernetes/GPU apps, DeepStream pipelines, Arc GitOps, or local ARM workloads, and other Azure Stack Edge related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure IoT Edge (use azure-iot-edge), Azure Local (use azure-local), Azure Virtual Machines (use azure-virtual-machines).

Validates observable behavior of apps, CLIs, APIs, and generated artifacts against a prewritten contract without inspecting source code.

Renders text and images as ASCII art using local CLI tools and free REST APIs, with no API keys required.

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly;...

Applies design engineering principles to polish UI components, covering typography, surfaces, animations, and performance with concrete CSS values and review checklists.

Segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks with zero-shot transfer.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Use when creating a note, document, script or project that shouldn't outlive a known date — interview prep, a migration, a course, a time-boxed experiment — so it gets deleted on time instead of rotting in the repository.

Trains, evaluates, and runs inference for Mask Auto-Label (MAL) weakly-supervised segmentation models using ViT-MAE backbones with minimal point or box annotations.

Performs a white-box source code security review structured around OWASP ASVS 5.0, mapping attack surfaces, tracing data flows, and chaining into downstream penetration testing and threat modeling skills.

Location intelligence — geocode a place, reverse-geocode coordinates, find nearby places (44 POI categories), driving/walking/cycling distance + time, turn-by-turn directions, timezone lookup, bounding box + area for a named place, and POI search within a rectangle. Uses OpenStreetMap + Overpass + OSRM. Free, no API key.

Design motion, transitions, and bespoke graphics for native Rust gpui apps — the framework behind Zed and the pd-console operator console. Use for with_animation, easing curves, BoxShadow/glow, breathing dots, pane expand/zoom/slide transitions, and custom paint/Vello/wgpu surfaces. Trigger on: gpui animation, with_animation, gpui transition, gpui easing, BoxShadow, gpui shadow, pd-console motion, gpui paint/canvas, Vello, wgpu, "lift/slide/zoom/spring in gpui", reduced-motion in a native Rust UI, "animation re-renders forever", repeat() never stops. NOT for: web/React motion (→ animation-system-architect), CLI/TUI output (→ beautiful-cli-design), general non-motion GUI layout/color/typography (→ beautiful-gui-design).

Analyzes the design language of Nintendo.com from 2001, documenting its brushed-periwinkle chrome interface, beveled metal plates, chamfered panel geometry, carbon-navy command layer, rationed warm accents, photographic hero fields, and box-art typography.

Guide for implementing GDPR-valid consent under Article 7 conditions and Article 4(11) definition. Covers five core requirements: freely given, specific, informed, unambiguous, and clear affirmative action. Includes pre-ticked boxes prohibition per Planet49 CJEU C-673/17, consent form audit checklist, and practical implementation patterns.

Create polished HTML technical reports and multi-page documents for consulting deliverables. Features a professional design system with dark cover pages, warm cream content pages, Switzer + Cartograph CF typography, and a curated component library (KPI cards, data tables, callout boxes, recommendation cards, priority banners). Outputs standalone HTML that prints cleanly to PDF.

WorkorAI talent marketplace skill: candidate job search and employer hiring with white-box match explanations via the WorkorAI MCP server (https://workorai.com/mcp). Use when the user asks to find a job, apply to jobs, respond to employer invitations, or when an employer wants to post jobs, search and evaluate candidates, invite them, and review applicants.

Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.

AI skill for building ApexCharts.js charts and data visualizations. Use when the user asks to create, configure, or troubleshoot any chart using ApexCharts (line, area, bar, pie, donut, radialBar, scatter, bubble, heatmap, candlestick, boxPlot, radar, polarArea, rangeBar, rangeArea, treemap). Covers correct data formats, lifecycle, formatters, tree-shaking, SSR, and framework integration. In React / Vue / Angular projects, prefer the framework wrapper packages (`react-apexcharts`, `vue3-apexcharts`, `ng-apexcharts`) over the core API.

Build editorial/magazine/report webpages on a GENUINE Müller-Brockmann modular grid (International Typographic Style), not a decorative one. Encodes the discipline — columns + modules + baseline, grotesque type, flush-left ragged-right, restrained black/white/red — AND the front-end engineering that makes the grid load-bearing: one CSS-variable source of truth, a grid-toggle overlay that lives in the SAME content box as the content, subgrid 'bands' so every element snaps to a column line, an 8px baseline lock, and runtime optical alignment that puts display type's ink (not its box) on the line. Ships a scaffold generator and a Puppeteer harness that proves 0px adherence. Use when: building any editorial, magazine, report, or longform page that must read as rigorously grid-aligned, Swiss, International Typographic Style, or 'Müller-Brockmann'. Triggers: magazine spread, grid system, Swiss design, editorial layout, show the grid, grid overlay toggle, baseline grid, modular grid, 12-column layout, 瑞士网格, 版面网格.

Create polished HTML technical reports and multi-page documents for consulting deliverables. Includes a customizable design system with dark cover page, warm cream content pages (8.5x11 portrait), professional typography, and a curated component library (KPI cards, data tables with severity coloring, callout boxes, recommendation cards, priority banners). Trigger on report, audit report, technical document, written report, assessment, or multi-page document. Outputs standalone HTML files exported to PDF via Chrome headless.

[devup-ui] Routing-first front door for Devup UI — a zero-runtime CSS-in-JS library whose Rust + WebAssembly preprocessor extracts every style at build time, so no styling JavaScript ships at runtime. Use to adopt/integrate Devup UI, wire the build-time plugin into Next.js/Vite/Rsbuild/Webpack/Bun, write styles with Box/css props or the styled-components-compatible styled() API, set up type-safe theming with devup.json, or migrate off styled-components/Emotion/Tailwind/Panda/vanilla-extract to zero runtime.

Establish CSS/Tailwind baseline quality — font smoothing, line-height defaults, box-sizing, focus rings, color contrast, Tailwind anti-patterns, and CSS containment. Use when asked to "set up CSS baseline", "Tailwind best practices", "fix font rendering", "global CSS foundation", "typography defaults", "CSS anti-patterns", "improve CSS quality", "line-height reset", "normalize styles", or reviewing a new project's base styles for correctness. Do NOT use for: full design system token architecture — see design-system-gen. Do NOT use for: component-level animation — see motion-design.

Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

Add, connect, and configure nodes in Blueprint event graphs and function graphs — function-call/event/branch/timer/custom-event/delegate nodes, Standard Macro nodes (ForEachLoop/ForLoop/WhileLoop/DoOnce/Gate/IsValid/FlipFlop via add_macro_instance_node), macro-graph creation, pin wiring, node layout, arrays, comment boxes, and the batch build_graph API. Use when the user asks to wire up Blueprint logic, add or connect nodes, add a loop/macro node, build an event graph, set up a timer/delay, broadcast a delegate, or lay out an existing graph.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.

Expert 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.