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 StamKavid/last-ds-mile --skill data-viz-standardsgit clone --depth 1 https://github.com/StamKavid/last-ds-mileWrote 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/stamkavid/last-ds-mile/data-viz-standards)<a href="https://agentmods.dev/skills/stamkavid/last-ds-mile/data-viz-standards"><img src="https://agentmods.dev/badge/skills/stamkavid/last-ds-mile/data-viz-standards/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/stamkavid/last-ds-mile/data-viz-standards"><img src="https://agentmods.dev/badge/skills/stamkavid/last-ds-mile/data-viz-standards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00069 | $0.01217 |
| Opus 5 | $0.00034 | $0.00609 |
| Sonnet 5 | $0.00014 | $0.00243 |
| Haiku 4.5 | $0.00007 | $0.00122 |
Grade A, and why
data-viz-standards 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 12d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data-viz-standards
Overview
The right visualization tool depends on who's looking and why — exploratory hypothesis testing and a stakeholder-facing table need different tools. Also covers the honesty checks that keep a chart from misleading its audience, intentionally or not.
When to Use
- Building plots during
/ds-exploreto test a hypothesis. - Preparing tables or charts for
/ds-evaluate's slice performance or/ds-report's stakeholder narrative. - A chart's axis, aggregation, or scale choice seems like it could mislead, even unintentionally.
- NOT for: deciding which slices/subgroups to analyze in the first place (see
error-analysis) — this skill is about how to present a finding, not which findings to look for.
Core Process
- Identify the audience first: exploratory (you, testing a hypothesis) or stakeholder-facing (someone deciding based on this). The right tool differs.
- For exploratory work, pick the chart type from the decision table below and state
the hypothesis it's testing (per
/ds-explore's own discipline) before building it. - For stakeholder-facing work, prefer a table (
great_tables) over a chart when the audience needs to read specific numbers, and a chart only when a pattern or trend is the point. - Before finalizing any chart or table, run the honesty checklist (below) — a technically-correct chart can still mislead through axis or aggregation choices.
Techniques/Patterns
Library choice by purpose
| Purpose | Recommended library | Why |
|---|---|---|
| Fast, hypothesis-driven EDA plots (interactive, notebook-embedded) | Altair | Declarative grammar-of-graphics — state the encoding (x, y, color, facet) directly, mirroring "state the hypothesis, then the chart" |
| Interactive drill-down / a dashboard | Plotly | Best interactivity and browser integration |
| Very large or streaming data | Bokeh | More efficient than Altair/Plotly at genuine scale |
| Stakeholder-facing tables (slice performance, model card figures, report numbers) | great_tables |
Purpose-built for publication-quality tables — currency/percent formatting, source notes, HTML/image export — a better fit than a chart when exact numbers matter |
Committed static evidence — a stage-doc figure saved to .last-ds-mile/figures/*.png, not viewed interactively |
matplotlib | Altair/Plotly's native output is an interactive spec, the wrong shape for a committed PNG; savefig is the direct path. Also what SHAP's own plotting functions render with, so figures share one library. |
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.
- 12d ago First seen · 88 lines · 69 tokens per session scan A eb6c7c0c5b2b
data-viz-standards is a skill published in the GitHub repository StamKavid/last-ds-mile (3 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,217 once invoked, about $0.0003 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.
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