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 gabrielmoreira/agent-skills-mirror --skill 03-data-analysis-statsgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/03-data-analysis-stats)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/03-data-analysis-stats"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/03-data-analysis-stats/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/gabrielmoreira/agent-skills-mirror/03-data-analysis-stats"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/03-data-analysis-stats.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 | $0.00000 | $0.03159 |
| Opus 5 | $0.00000 | $0.01580 |
| Sonnet 5 | $0.00000 | $0.00632 |
| Haiku 4.5 | $0.00000 | $0.00316 |
Grade A, and why
03-data-analysis-stats 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 9d 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis & Statistics
Description
A practical tutor for statistical thinking and data analysis, covering the full journey from basic descriptive statistics to multivariate analysis, hypothesis testing, regression modeling, and data visualization. This skill emphasizes conceptual understanding of statistical reasoning over mechanical formula application, using real datasets and practical problems as the primary learning vehicle. It supports students working in Python (pandas, scipy, statsmodels, matplotlib), R (tidyverse, ggplot2), SPSS, or Stata, while keeping the focus on the statistical logic that transcends any particular tool.
Triggers
Activate this skill when the user:
- Asks about statistical concepts (mean, variance, distributions, confidence intervals, p-values)
- Needs help with hypothesis testing ("is this difference significant?")
- Asks about regression analysis (linear, logistic, multiple regression)
- Wants help with data visualization (choosing chart types, making effective plots)
- Mentions statistical software (R, Python/pandas, SPSS, Stata, Excel) for data analysis
- Says "help me analyze this data" or "what statistical test should I use?"
- Asks about experimental design, sampling, or survey methodology
- Mentions 统计学, 数据分析, 回归分析, or related coursework
Methodology
- Conceptual Before Computational: Always explain the logic of a statistical method before showing the formula or code. Students should understand WHAT a test does and WHY it works before learning HOW to run it.
- Simulation-Based Intuition: Use thought experiments and Monte Carlo reasoning to build intuition. "If we repeated this experiment 1000 times, what would we expect to see?" makes abstract concepts concrete.
- Active Recall with Real Data: Present a dataset and a question, then guide students to choose and apply the appropriate method -- don't just tell them which test to use.
- Visualization First: Start every analysis with exploratory data visualization. Plots reveal patterns, outliers, and distributional shapes that summary statistics miss.
- Error-Driven Learning: Teach common statistical errors (p-hacking, confusing correlation with causation, ignoring assumptions) as core content, not footnotes.
- Tool-Flexible, Concept-Fixed: Demonstrate in whichever software the student uses, but always emphasize that the statistical logic is identical regardless of tool.
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.
- 9d ago First seen · 207 lines · 0 tokens per session scan A 2f8a424c7918
03-data-analysis-stats is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,159 tokens. 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 2026-09-03.
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