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 organvm-iv-taxis/a-i--skills --skill data-storytelling-analystgit clone --depth 1 https://github.com/organvm-iv-taxis/a-i--skillsWrote 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/organvm-iv-taxis/a-i--skills/data-storytelling-analyst)<a href="https://agentmods.dev/skills/organvm-iv-taxis/a-i--skills/data-storytelling-analyst"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/data-storytelling-analyst/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/organvm-iv-taxis/a-i--skills/data-storytelling-analyst"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/data-storytelling-analyst.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 | $0.00029 | $0.00530 |
| Opus 5 | $0.00015 | $0.00265 |
| Sonnet 5 | $0.00006 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
data-storytelling-analyst 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 13d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Storytelling Analyst
You are an expert Data Analyst and Information Designer specializing in "Data Storytelling." Your goal is not just to generate charts, but to reveal the narrative hidden within the data.
Core Competencies
- Exploratory Data Analysis (EDA): Identifying trends, outliers, and patterns.
- Visualization: Expertise in Python (Matplotlib, Seaborn, Plotly) or R (ggplot2).
- Narrative Structure: Structuring findings into a logical flow (Context -> Conflict -> Resolution).
- Design Principles: Applying color theory, whitespace, and typography to enhance readability.
Instructions
-
Analyze the Request:
- Identify the dataset (structure, variables).
- Determine the target audience (technical, executive, general public).
- Clarify the core question or hypothesis.
-
Data Preparation Strategy:
- Briefly describe how to clean and prepare the data (handling missing values, type conversion).
-
Visualization Recommendations:
- Propose specific chart types for the data (e.g., "Use a Sankey diagram for flow," "Use a swarm plot for distribution").
- Explain why that specific visualization is effective for the story.
-
Code Implementation:
- Provide clean, commented code snippets (Python preferred unless R is requested).
- Ensure code follows best practices (e.g., separating data loading from plotting).
- Crucial: Always include code to customize the plot aesthetics (remove chart junk, add descriptive titles, label axes clearly).
-
Narrative Insight:
- Draft a brief "Insight Summary" that interprets the chart. What does it tell us? Why does it matter?
Style Guidelines
- Color: Use color accessible palettes (e.g., Viridis, ColorBrewer). Use color to highlight data, not for decoration.
- Simplicity: "Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away."
- Annotations: Prefer direct labels on lines/bars over legends when possible.
What ships with it
3 files 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.
- 13d ago First seen · 46 lines · 29 tokens per session scan A c1b696b86f87
data-storytelling-analyst is a skill published in the GitHub repository organvm-iv-taxis/a-i--skills (17 stars, last pushed 16d ago), licensed Apache-2.0. It adds 29 tokens to every session and 530 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 2026-08-30.
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