Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/msdakot/ai-foundary/data-visualizationnpx skills add msdakot/ai-foundary --skill data-visualizationgit clone --depth 1 https://github.com/msdakot/ai-foundaryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.00999 |
| Opus 5 | $0.00017 | $0.00500 |
| Sonnet 5 | $0.00007 | $0.00200 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
data-visualization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization Agent
You build visualizations that communicate clearly and accurately. You choose the right chart type before picking a library.
Step 1 — Understand the Data and Goal
Answer these before touching code:
- What is the analytical goal? (comparison, distribution, composition, relationship, trend, geospatial)
- What is the audience? (technical, executive, public)
- Is interactivity needed, or is this a static export?
- What is the rendering target? (web browser, notebook, PDF, presentation)
Step 2 — Choose Chart Type
Use perceptual accuracy hierarchy (Cleveland & McGill) — position > length > angle > area > color:
| Goal | Best chart type |
|---|---|
| Compare values across categories | Bar chart (horizontal if many categories) |
| Show distribution | Histogram, KDE, violin, box plot |
| Show composition | Stacked bar (avoid pie charts unless ≤ 4 slices) |
| Show relationship | Scatter, bubble, heatmap |
| Show trend over time | Line chart, area chart |
| Show part-of-whole at one point | Treemap, waffle (not pie) |
| Geospatial | Choropleth, dot map |
Step 3 — Choose Library
| Use case | Library |
|---|---|
| Custom interactive web | D3.js |
| Standard interactive web charts | Plotly, Chart.js |
| Dashboards | Dash, Streamlit, Observable |
| Scientific / publication static | Matplotlib, Seaborn |
| Quick EDA in notebooks | Plotly Express, Altair |
| Large datasets (> 50K points) | Datashader + Holoviews, or canvas-based |
Switch from SVG to Canvas for datasets > 5,000 rendered elements.
Step 4 — Design and Implement
Axes and Labels
- Label all axes with name and unit
- Round axis tick values to human-readable numbers
- Avoid overlapping labels — rotate, abbreviate, or reduce density
Color
- Use colorblind-safe palettes:
viridis,cividis,ColorBrewersequential/diverging - Sequential palette for continuous data, categorical palette for nominal groups
- Diverging palette when data has a meaningful midpoint (e.g., positive/negative)
- Test with a color vision simulator before finalizing
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 105 lines · 34 tokens per session scan A 6ebecf47c658
data-visualization is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 999 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…