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 itallstartedwithaidea/agent-skills --skill data-analysisgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/itallstartedwithaidea/agent-skills/data-analysis)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/data-analysis/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/itallstartedwithaidea/agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/data-analysis.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 | $0.00032 | $0.01340 |
| Opus 5.5 | $0.00013 | $0.00536 |
| Sonnet 5.5 | $0.00006 | $0.00268 |
| Haiku 4.5 | $0.00003 | $0.00134 |
Grade A, and why
data-analysis 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 1mo 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
Part of Agent Skills™ by googleadsagent.ai™
Description
Data Analysis provides a structured framework for statistical analysis using pandas, numpy, and scipy, with visualization through matplotlib, seaborn, and plotly. The agent follows a rigorous pipeline from data ingestion and cleaning through exploratory analysis, statistical testing, and publication-quality visualization, ensuring reproducibility at every step.
Scientific data analysis is not exploratory coding—it is a disciplined process where every transformation is justified, every statistical test has verified assumptions, and every visualization accurately represents the underlying data. This skill enforces that discipline by requiring the agent to document data provenance, validate distributions before applying parametric tests, and report effect sizes alongside p-values.
The visualization layer produces figures suitable for journal submission: proper axis labels with units, colorblind-safe palettes, appropriate figure sizes for single or double-column layouts, and vector output formats (SVG, PDF). Interactive plotly visualizations are generated for exploratory work; static matplotlib/seaborn figures for publication.
Use When
- Performing statistical analysis on experimental or observational data
- Cleaning and transforming datasets for downstream analysis
- Creating publication-quality figures and plots
- Running hypothesis tests with proper assumption checking
- Exploratory data analysis on new datasets
- Building reproducible analysis pipelines
How It Works
graph TD
A[Raw Data] --> B[Ingest + Validate Schema]
B --> C[Clean: Missing Values, Outliers, Types]
C --> D[Exploratory Data Analysis]
D --> E[Distribution Assessment]
E --> F{Parametric Assumptions Met?}
F -->|Yes| G[Parametric Tests]
F -->|No| H[Non-Parametric Tests]
G --> I[Effect Size + Confidence Intervals]
H --> I
I --> J[Publication Visualization]
J --> K[Reproducible Report]
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.
- 1mo ago First seen · 135 lines · 32 tokens per session scan A ddc198f745c3
data-analysis is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 1,340 once invoked, about $0.0001 per session on Opus 5.5. 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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