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 Dragoon0x/everything-design-taste --skill data-storytellinggit clone --depth 1 https://github.com/Dragoon0x/everything-design-tasteWrote 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/dragoon0x/everything-design-taste/data-storytelling)<a href="https://agentmods.dev/skills/dragoon0x/everything-design-taste/data-storytelling"><img src="https://agentmods.dev/badge/skills/dragoon0x/everything-design-taste/data-storytelling/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/dragoon0x/everything-design-taste/data-storytelling"><img src="https://agentmods.dev/badge/skills/dragoon0x/everything-design-taste/data-storytelling.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 | $0.00018 | $0.00257 |
| Opus 5 | $0.00009 | $0.00129 |
| Sonnet 5 | $0.00004 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
data-storytelling 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 7d 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.
What it actually says
Data Storytelling
Data Narrative Principles
- Lead with the insight, not the data. "Sales doubled in Q3" before showing the chart.
- One story per chart. If a chart tells two stories, make two charts.
- Annotate the interesting parts. Labels on peaks, dips, and inflection points.
- Provide context. "42% increase" compared to what? Industry average? Last year? Goal?
- Design for the audience. Executives want conclusions. Analysts want data.
Chart Annotation
- Callout labels on data points that matter.
- Reference lines for targets/goals/averages.
- Shaded regions for time periods of interest.
- Brief text annotations explaining anomalies.
Presentation Order
- Headline: The conclusion in one sentence.
- Chart: Visual evidence.
- Context: Comparison, trend, benchmark.
- Implication: What this means for the business.
- Action: What to do about it.
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.
- 7d ago First seen · 28 lines · 18 tokens per session scan A ead032d6e55b
data-storytelling is a skill published in the GitHub repository Dragoon0x/everything-design-taste (11 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 257 once invoked, about $0.0001 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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