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 rules/thatrebeccarae/claude-marketing/data-viz-deckgit clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote 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/rules/thatrebeccarae/claude-marketing/data-viz-deck)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/data-viz-deck"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/data-viz-deck.svg" alt="Measured on agentmods" 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 | $0.00063 | $0.01765 |
| Opus 5 | $0.00032 | $0.00882 |
| Sonnet 5 | $0.00013 | $0.00353 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
data-viz-deck 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 6d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization & Deck Builder
Transform structured data and analysis into polished visual deliverables: presentation decks, interactive dashboards, and visual reports.
When to Use This Skill
- User has completed an audit or analysis and wants a visual deliverable
- User says "make a deck," "create a presentation," "build a dashboard," "visualize this"
- User wants to turn a markdown report into client-ready slides
- User needs charts, tables, or visual summaries from performance data
Output Formats
1. PPTX Deck (Primary)
Native PowerPoint with editable charts, styled tables, and professional layouts. Best for client handoffs and presentations.
Requires: python-pptx (installed), pandas (installed)
2. Interactive HTML Dashboard
Single-file HTML with plotly.js charts (loaded via CDN), filterable tables, and responsive layout. Best for sharing interactive reports.
Requires: jinja2 (installed), pandas (installed). Plotly.js loaded via CDN at runtime.
3. Visual Markdown Report
Enhanced markdown with embedded chart images (requires matplotlib: pip install matplotlib). Best for vault reports and documentation.
Workflow
Step 1: Identify the Data Source
Read the source file (audit markdown, CSV, JSON, or database query results). Parse the key metrics, tables, and findings into a pandas DataFrame or structured dict.
Step 2: Select Chart Types
| Data Pattern | Chart Type | When to Use |
|---|---|---|
| Categories with values | Bar chart (horizontal) | Revenue by category |
| Categories + benchmark | Bar chart with reference line | Performance vs benchmark |
| Parts of a whole | Doughnut/Pie chart | Revenue concentration, channel mix |
| Values over time | Line chart | Trend data, period-over-period |
| Two variables | Scatter plot | Correlation analysis |
| Performance scoring | Heatmap table | Color-coded metrics (green/amber/red) |
| Before/after or gaps | Waterfall chart | Revenue opportunity sizing |
| Ranked items | Horizontal bar | Top 10 sorted |
| Funnels | Funnel chart | Delivered > opened > clicked > converted |
| Status overview | Scorecard/KPI tiles | Executive summary metrics |
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.
- 6d ago First seen · 163 lines · 63 tokens per session scan A 605b050d9d4f
data-viz-deck is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (131 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,765 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 2026-08-30.
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