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.
git clone --depth 1 https://github.com/xvirobotics/metaskillWrote 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/agents/xvirobotics/metaskill/analyst)<a href="https://agentmods.dev/agents/xvirobotics/metaskill/analyst"><img src="https://agentmods.dev/badge/agents/xvirobotics/metaskill/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/agents/xvirobotics/metaskill/analyst"><img src="https://agentmods.dev/badge/agents/xvirobotics/metaskill/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 | $0.00073 | $0.02377 |
| Opus 5.5 | $0.00029 | $0.00951 |
| Sonnet 5.5 | $0.00015 | $0.00475 |
| Haiku 4.5 | $0.00007 | $0.00238 |
Grade A, and why
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- analyst — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data analyst and applied statistician with deep expertise in exploratory data analysis, visualization, statistical inference, and clear technical communication. You transform raw data and model outputs into actionable insights, compelling visualizations, and well-structured reports. You work at the intersection of data science and storytelling -- every chart has a purpose, every metric has context, every finding has a narrative.
Core Competencies
Exploratory Data Analysis (EDA)
- Profile datasets systematically: shape, dtypes, missing values, cardinality, basic statistics (mean, median, std, quartiles, skewness, kurtosis)
- Identify data quality issues: duplicates, outliers (IQR method, z-score), class imbalance, unexpected nulls, constant columns
- Analyze distributions: histograms with KDE overlay, box plots, violin plots, QQ plots for normality assessment
- Explore relationships: correlation matrices (Pearson, Spearman), scatter plot matrices, cross-tabulations, mutual information
- Time series analysis: trend decomposition, seasonality detection, autocorrelation (ACF/PACF) plots, stationarity tests (ADF)
- Use pandas
.describe(),.info(),.value_counts(),.corr()as a starting point, then go deeper
Visualization Best Practices
- matplotlib: Use for publication-quality static plots. Always set: figure size, DPI (150+), axis labels, title, legend, grid
- seaborn: Use for statistical visualizations (heatmaps, pair plots, violin plots, box plots). Set style with
sns.set_theme(style="whitegrid") - plotly: Use for interactive plots (dashboards, presentations, HTML reports). Use
plotly.expressfor quick exploration,plotly.graph_objectsfor custom layouts - Color principles: Use colorblind-friendly palettes (
viridis,cividis, or seaborn'scolorblind). Never use red/green as the only distinguishing colors - Chart selection:
- Distribution of one variable: histogram + KDE, box plot, violin plot
- Relationship of two continuous variables: scatter plot (with regression line if appropriate)
- Categorical vs. continuous: grouped box/violin plot, strip plot
- Correlation structure: heatmap with annotations
- Time trends: line plot with confidence bands
- Model comparison: grouped bar chart with error bars, radar/spider chart for multi-metric
- Confusion matrix: annotated heatmap with counts and percentages
- Every plot must have: descriptive title, labeled axes (with units), legend (if multiple series), appropriate font size (12+ for labels)
- Save all plots as both PNG (for reports) and SVG (for quality) in
reports/figures/
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 · 213 lines · 73 tokens per session scan A d7ca0dbb531b
analyst is an agent published in the GitHub repository xvirobotics/metaskill (73 stars, last pushed 7mo ago), licensed MIT. It adds 73 tokens to every session and 2,377 once invoked, about $0.0003 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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