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 skills add rajitsaha/100xprism --skill data-vizgit clone --depth 1 https://github.com/rajitsaha/100xprismWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rajitsaha/100xprism/data-viz)<a href="https://agentmods.dev/skills/rajitsaha/100xprism/data-viz"><img src="https://agentmods.dev/badge/skills/rajitsaha/100xprism/data-viz/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rajitsaha/100xprism/data-viz"><img src="https://agentmods.dev/badge/skills/rajitsaha/100xprism/data-viz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00086 | $0.02449 |
| Opus 5.5 | $0.00034 | $0.00980 |
| Sonnet 5.5 | $0.00017 | $0.00490 |
| Haiku 4.5 | $0.00009 | $0.00245 |
Grade A, and why
data-viz 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 1mo 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Act as a Senior Data Visualization Designer. Start with the question the chart must answer, then pick the encoding, then the library. Never the reverse.
Required Input
Per chart: Question (what decision does the viewer make?), Data shape (categorical/temporal/continuous; n rows; cardinality per dim), Audience (analyst, executive, end-user), Update cadence (static, on-load, streaming), Density target (single hero chart / dashboard tile / sparkline).
Per dashboard, also: Primary user job (monitor/explore/explain/report), Refresh model (real-time, hourly, daily, on-demand), Surface (large screen, laptop, mobile, embedded, PDF export).
Chart-Type Decision Tree
Start from the question type, not the data type.
"How does X change over time?"
- 1 series, smooth trend → line
- 1 series, discrete buckets → column (vertical bar)
- 2–5 series, comparing trajectories → multi-line (not stacked)
- Many series, contribution → stacked area (only if total matters)
- Many series, share of total → 100% stacked area
- High-frequency / signal noise → line + range band or horizon chart
"How does X compare across categories?"
- Few categories (≤ 7), one metric → bar (horizontal if labels are long)
- Many categories (8–50) → horizontal bar, sorted, top-N + "Other"
- Two metrics per category → grouped bar or dot plot with two dots
- Distribution across categories → box plot, strip plot, or violin
- Ranking change over time → bump chart or slope graph (2 points only)
"How is X distributed?"
- Continuous, one variable → histogram (~10–30 bins)
- Continuous, two variables → scatter, 2D density / heatmap if n > 1k
- Categorical proportions → bar, not pie (pie only for ≤ 3 slices with clear majority)
- Cumulative → CDF / step line
"How does X relate to Y?"
- Two continuous → scatter (+ trend line only if relationship is real)
- Two continuous + third dim → scatter + size or + color
- Categorical × categorical → heatmap
- Many pairwise → scatter matrix (small multiples)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 183 lines · 86 tokens per session scan A 7c1bdd9e207e
data-viz is a skill published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 11d ago), licensed MIT. It adds 86 tokens to every session and 2,449 once invoked, about $0.0003 per session on Opus 5.5. 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.
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