Results for “visualization”

Create R-based pictogram glyphs for skill, agent, or team icons in the visualization layer. Covers concept sketching, ggplot2 layer composition using the primitives library, color strategy, registration in the appropriate glyph mapping file and manifest, rendering via the build pipeline, and visual verification of the neon-glow output. Use when a new entity has been added and needs a visual icon for the force-graph visualization, an existing glyph needs replacement, or when batch-creating glyphs for a new domain.

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Chọn đúng biểu đồ, kể chuyện với số liệu, và tránh误导 — dựa trên nguyên tắc từ Kieran Healy (Data Visualization, Princeton 2019) và taxonomy từ AntV chart-visualization-skills. Dùng khi cần quyết định loại chart, trình bày insight cho stakeholder, hoặc kiểm tra xem figure có gây hiểu lầm không. Để vẽ Python thực tế, chuyển sang matplotlib / seaborn / scientific-visualization.

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Expert in visualizing and explaining braid theory, knot mathematics, and topological concepts for educational purposes. Use for creating interactive visualizations, explainer cards, step-wise animations, and translating abstract algebra into intuitive understanding. Activate on keywords: braid theory, knot visualization, σ notation, crossing diagrams, Yang-Baxter, topological education. NOT for general math tutoring, pure knot invariant computation, or non-educational knot theory research.

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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

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Publication-quality statistical visualization with mandatory best practices. Use when creating plots, charts, figures, or any data visualization. Enforces accessibility, uncertainty display, proper labeling, and statistical accuracy.

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Create, standardize, or update step-by-step algorithm visualizations for a given problem ID. Use when the user mentions 'visual', 'visualization', 'animate', or 'step-by-step' alongside a problem ID in /problem/:id format.

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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

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Turn audio into spectrograms, images showing frequencies over time, and panels of features such as loudness and rhythm. Combine views in a grid and choose a time segment, color palette, and output image size.

• Viewing audio frequencies over time
• Comparing loudness and rhythm in a panel grid
• Visualizing a selected audio segment

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.

• Build gene trees from related nucleotide sequences
• Compare evolutionary relationships within protein families
• Review branch support and analysis logs

Turn documents, notes, research, or code into a knowledge graph that maps identifiable things and their relationships. Create or update entity files and typed connections, with source evidence attached for inspection and later visualization.

• Map people and organizations across documents
• Organize research with traceable evidence
• Map relationships between software modules

Map threat groups and their attack techniques to MITRE ATT&CK, a catalog of adversary behavior. Generate Navigator visualization files, compare groups, and overlay detection coverage to identify gaps that can guide defensive planning.

• Profiling the attack techniques used by a specific threat group
• Comparing techniques across multiple threat groups
• Finding detection gaps against a target adversary

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Choose suitable charts for trends, comparisons, distributions, and relationships, then create them in Python with matplotlib, seaborn, or plotly. Apply guidance on colors, labels, scales, and accessibility to produce publication-quality figures or interactive charts.

• Choose a chart type that fits the data and message
• Create figures for reports or publications
• Build interactive charts with Plotly

Create mathematical and technical explainer videos with Manim Community Edition. The skill guides scene planning, animation code, rendering, and clip assembly to show equations, algorithms, system structures, and data through coordinated visuals.

• Explaining mathematical concepts through animation
• Showing equation derivations step by step
• Visualizing algorithm execution

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.

• Monitoring training loss and accuracy
• Comparing experiments with different training settings
• Inspecting weight and gradient distributions

Continue library-dependent Python tasks when sandbox execution fails by switching to a shell script. Covers recognizing import errors, preserving script text, checking execution output, and troubleshooting package availability for tasks such as PDF generation or visualization.

• Retrying Python code after missing-library errors
• Continuing PDF generation through shell execution
• Running visualization code with installed packages

Build charts from tables using Vega-Lite, Vega, or Observable Plot. Describe data, visual elements, and interactions in reusable definitions, choose a suitable tool, and assess mobile layout, performance, and when custom rendering becomes necessary.

• Writing a Vega-Lite chart specification from a dataset
• Choosing between Plot, Vega-Lite, and D3
• Building layered charts with panels and tooltips

Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.

• Choosing a chart to show a data distribution
• Presenting confidence intervals clearly
• Revealing variation hidden by dashboard averages

Integrate charts and architecture diagrams into React or Next.js apps. Plan which parts run in the browser, separate rendering from application state, and address loading performance, mobile layouts, accessibility, and shareable views.

• Integrating D3 charts into React components
• Choosing browser or server rendering in Next.js
• Sharing chart filters and selections through URLs

Design, review, and implement Gantt charts for project schedules. Assess source data, task dependencies, and resource needs, then define suitable views, editing behavior, rendering tools, accessibility, exports, and tests—or recommend a clearer alternative.

• Choose between a Gantt chart, Kanban board, or timeline
• Map project-management exports into schedule data
• Review dependencies, critical path, and baseline variance

Decide whether geographic data needs a map, then choose suitable background maps, visual forms, and implementation tools. Plan overlapping markers, zoom behavior, and mobile interactions so the map supports the intended analysis or product task.

• Deciding whether regional comparisons need a map
• Designing regional maps with meaningful color scales
• Planning overlapping markers and zoom behavior

Review charts, diagrams, and dashboards for accessibility, including color contrast, keyboard access, and text alternatives. Get specific guidance for preserving key findings in screen readers, static exports, and reduced-motion views.

• Reviewing dashboard colors and keyboard access
• Writing chart alt text and long descriptions
• Preparing accessible figures for PDF reports

Create clear, accurate charts for dashboards, data exploration, and reports. Choose chart types that fit the message, refine labels and scales, use accessible colors, and add context and annotations to help readers understand the findings.

• Designing a sales dashboard for executives
• Exploring patterns in customer behavior
• Creating charts for an annual report