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 charlieviettq/awesome-agent-skill --skill data-viz-storytelling-healygit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/data-viz-storytelling-healy)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/data-viz-storytelling-healy"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-viz-storytelling-healy/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/charlieviettq/awesome-agent-skill/data-viz-storytelling-healy"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-viz-storytelling-healy.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 | $0.00105 | $0.02591 |
| Opus 5 | $0.00053 | $0.01295 |
| Sonnet 5 | $0.00021 | $0.00518 |
| Haiku 4.5 | $0.00011 | $0.00259 |
Grade A, and why
data-viz-storytelling-healy 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 9d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization & Storytelling (Healy + AntV)
"The tools you use can help you live up to the right standards. But they cannot make you do the right thing." — Kieran Healy, Data Visualization, Ch. 1
When to Use This Skill
- Deciding which chart type fits the analytical question
- Writing a report or slide deck where numbers need a narrative
- Reviewing a figure for honesty / misleading patterns
- Drafting an insight summary with claim → evidence → caveat structure
- Choosing between infographic (visual design) and statistical figure (accuracy)
For actual plotting code → use matplotlib, seaborn, or scientific-visualization.
Section 1 — Honesty & Judgment (Healy Ch. 1)
Before choosing colors or chart types, verify the figure does not mislead.
1.1 Pre-plot Honesty Checklist
| Check | Why it matters |
|---|---|
| Baseline / zero start | Bar charts starting above zero exaggerate differences. Line charts may omit zero legitimately if the focus is trend, not level. |
| Dual axes | Two Y-axes on one plot invite false correlation. Prefer faceted panels or indexed series. |
| Cherry-picked window | Short time windows can hide long-term patterns. Always show context. |
| Aggregation level | Averages can hide distribution shape. Consider showing raw data, box plots, or density. |
| Proportional vs absolute | Normalize when comparing groups of different size; keep raw counts available. |
| Color encoding | Do not use rainbow/palette that implies order for categorical data. Use colorblind-safe palettes. |
| Uncertainty | Show CI, prediction bands, or error bars when presenting estimates. |
| Axis labels & units | Every axis must state what it measures and in what unit. |
Rule of thumb: If removing context (time range, N, CI) changes the takeaway, the figure is misleading as-is.
1.2 Perception Principles (Healy Ch. 1 + Cleveland)
Humans perceive some encodings more accurately than others:
- Position on common scale — most accurate (scatter, line)
- Position on different scale — good (grouped bar, faceted)
- Length / direction — moderate
- Angle / area — poor (avoid pie charts for comparison)
- Color intensity / shape — worst for magnitude; OK for categories
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.
- 9d ago First seen · 227 lines · 105 tokens per session scan A 7eb2ced29ccd
data-viz-storytelling-healy is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 2,591 once invoked, about $0.0005 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 2026-09-03.
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