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/warroom-ceo/core/data-visualizationnpx skills add WARROOM-CEO/CORE --skill data-visualizationgit clone --depth 1 https://github.com/WARROOM-CEO/COREWhat 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 | $0.00059 | $0.02892 |
| Opus 5 | $0.00030 | $0.01446 |
| Sonnet 5 | $0.00012 | $0.00578 |
| Haiku 4.5 | $0.00006 | $0.00289 |
Grade A, and why
data-visualization-th 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.
This is a copy
86% identical to data-visualization — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Language: All user-facing output — responses, summaries, and any text the user will read — must be written in Thai (ภาษาไทย). Internal logic, file paths, code snippets, and technical values remain in English.
Data Visualization Skill
Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.
Chart Selection Guide
Choose by Data Relationship
| What You're Showing | Best Chart | Alternatives |
|---|---|---|
| Trend over time | Line chart | Area chart (if showing cumulative or composition) |
| Comparison across categories | Vertical bar chart | Horizontal bar (many categories), lollipop chart |
| Ranking | Horizontal bar chart | Dot plot, slope chart (comparing two periods) |
| Part-to-whole composition | Stacked bar chart | Treemap (hierarchical), waffle chart |
| Composition over time | Stacked area chart | 100% stacked bar (for proportion focus) |
| Distribution | Histogram | Box plot (comparing groups), violin plot, strip plot |
| Correlation (2 variables) | Scatter plot | Bubble chart (add 3rd variable as size) |
| Correlation (many variables) | Heatmap (correlation matrix) | Pair plot |
| Geographic patterns | Choropleth map | Bubble map, hex map |
| Flow / process | Sankey diagram | Funnel chart (sequential stages)-th |
| Relationship network | Network graph | Chord diagram |
| Performance vs. target | Bullet chart | Gauge (single KPI only) |
| Multiple KPIs at once | Small multiples | Dashboard with separate charts |
When NOT to Use Certain Charts
- Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
- 3D charts: Never. They distort perception and add no information.
- Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
- Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
- Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.
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 · 308 lines · 59 tokens per session scan A 5c72c400bc3c
data-visualization-th is a skill published in the GitHub repository WARROOM-CEO/CORE (30 stars, last pushed 5mo ago), licensed MIT. It adds 59 tokens to every session and 2,892 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to data-visualization, differing in 10 lines, and is treated as a copy.
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