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 travisjneuman/.claude --skill data-sciencegit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/travisjneuman/.claude/data-science)<a href="https://agentmods.dev/skills/travisjneuman/.claude/data-science"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/data-science/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/travisjneuman/.claude/data-science"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/data-science.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 | $0.00047 | $0.01874 |
| Opus 5.5 | $0.00019 | $0.00750 |
| Sonnet 5.5 | $0.00009 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00187 |
Grade A, and why
data-science 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 26d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Science Expert
Comprehensive data science frameworks for analytics, machine learning, and data-driven decision making.
Data Strategy
Data Maturity Model
| Level | Name | Characteristics |
|---|---|---|
| 1 | Ad Hoc | Manual, inconsistent, siloed |
| 2 | Opportunistic | Some automation, point solutions |
| 3 | Systematic | Defined processes, governance emerging |
| 4 | Differentiating | Data-driven decisions, advanced analytics |
| 5 | Transformative | AI-first, competitive advantage |
Analytics Value Chain
DATA → INFORMATION → INSIGHT → ACTION → VALUE
PROGRESSION:
Descriptive: What happened?
Diagnostic: Why did it happen?
Predictive: What will happen?
Prescriptive: What should we do?
Autonomous: Self-optimizing systems
Statistical Analysis
Descriptive Statistics
CENTRAL TENDENCY:
- Mean: Sum / Count (sensitive to outliers)
- Median: Middle value (robust to outliers)
- Mode: Most frequent value
DISPERSION:
- Range: Max - Min
- Variance: Average squared deviation
- Standard Deviation: √Variance
- IQR: Q3 - Q1 (robust)
DISTRIBUTION SHAPE:
- Skewness: Asymmetry (0 = symmetric)
- Kurtosis: Tail heaviness (3 = normal)
For detailed inferential statistics and hypothesis testing, see Statistical Methods Reference.
Machine Learning
Algorithm Selection
| Task | Algorithms | When to Use |
|---|---|---|
| Classification | Logistic Regression, Random Forest, XGBoost, Neural Networks | Categorical outcomes |
| Regression | Linear Regression, Ridge/Lasso, Random Forest, XGBoost | Continuous outcomes |
| Clustering | K-Means, Hierarchical, DBSCAN | Group discovery |
| Dimensionality Reduction | PCA, t-SNE, UMAP | Feature reduction, visualization |
| Anomaly Detection | Isolation Forest, One-Class SVM, Autoencoders | Outlier detection |
| Time Series | ARIMA, Prophet, LSTM | Sequential data |
| Recommendation | Collaborative Filtering, Content-Based, Matrix Factorization | Personalization |
| NLP | Transformers, BERT, GPT | Text understanding/generation |
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.
- 26d ago Changed 85f0a59aef37
- 1mo ago First seen · 288 lines · 47 tokens per session scan A 9680ac9e317a
data-science is a skill published in the GitHub repository travisjneuman/.claude (106 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,874 once invoked, about $0.0002 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 2026-09-03.
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