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 femto/minion --skill data-analysisgit clone --depth 1 https://github.com/femto/minionWrote 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/femto/minion/data-analysis)<a href="https://agentmods.dev/skills/femto/minion/data-analysis"><img src="https://agentmods.dev/badge/skills/femto/minion/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/femto/minion/data-analysis"><img src="https://agentmods.dev/badge/skills/femto/minion/data-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00009 | $0.00549 |
| Opus 5.5 | $0.00004 | $0.00220 |
| Sonnet 5.5 | $0.00002 | $0.00110 |
| Haiku 4.5 | $0.00001 | $0.00055 |
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Skill
Description
This skill helps analyze datasets and create meaningful visualizations. It can handle CSV files, perform statistical analysis, and generate various types of plots.
Usage Instructions
When a user requests data analysis:
- Load the dataset: Use pandas to read the data file
- Inspect the data: Check shape, columns, data types, and basic statistics
- Clean the data: Handle missing values and outliers if necessary
- Perform analysis: Calculate relevant statistics based on user's question
- Create visualizations: Generate appropriate plots (line, bar, scatter, etc.)
- Save results: Export results and visualizations
Available Resources
Scripts
-
scripts/analyze.py: Core analysis functions
load_dataset(filepath): Load data from various formatsbasic_statistics(df): Calculate descriptive statisticsdetect_outliers(df, column): Identify outlierscorrelation_analysis(df): Compute correlations
-
scripts/visualize.py: Visualization utilities
plot_distribution(df, column): Create distribution plotsplot_correlation_matrix(df): Visualize correlation heatmapplot_time_series(df, date_col, value_col): Time series plotssave_plot(fig, filename): Save figure to file
References
- references/examples.md: Usage examples and common patterns
- references/best_practices.md: Data analysis best practices
Example Prompts
- "Analyze this CSV file and show me the trends"
- "Create a visualization of the sales data by month"
- "Find correlations in this dataset"
- "Identify outliers in the price column"
- "Generate a statistical summary of the data"
Output Format
Analysis results should include:
- Data overview (shape, columns, types)
- Statistical summary
- Key insights and findings
- Visualizations (saved as PNG files)
- Recommendations or next steps
Notes
- Always inspect data before analysis
- Handle missing values appropriately
- Choose visualizations that match the data type
- Provide clear explanations of findings
- Save all outputs for user reference
What ships with it
1 file 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.
- 1mo ago First seen · 72 lines · 9 tokens per session scan A a7c33f4f9b4f
data-analysis is a skill published in the GitHub repository femto/minion (151 stars, last pushed 10d ago), licensed MIT. It adds 9 tokens to every session and 549 once invoked, about $0.0000 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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