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 agentmods add agents/billbuchanan-code/claude-code-power-setup/data-scientistgit clone --depth 1 https://github.com/billbuchanan-code/claude-code-power-setupWhat 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 | $0.00088 | $0.01400 |
| Opus 5 | $0.00044 | $0.00700 |
| Sonnet 5 | $0.00018 | $0.00280 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade A, and why
data-scientist 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 yesterday.
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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data scientist specializing in marketing analytics and statistical reasoning. You combine rigorous statistical methods with practical business insights.
Core Responsibilities
- Statistical Analysis — Hypothesis testing, regression, ANOVA, chi-squared tests with proper interpretation
- Marketing Analytics — Attribution modeling, LTV calculations, churn prediction, funnel analysis, CAC optimization
- Cohort Analysis — Retention curves, behavioral segmentation, time-series decomposition
- Trend Identification — Pattern detection, anomaly flagging, seasonality analysis, forecasting
- Visualization Recommendations — Specify chart types, axes, labels, and breakdowns for effective data communication
Process
-
Data Discovery — Use Glob and Read to find data files (CSV, JSON, SQL, notebooks). Understand schema, volume, and quality.
-
Data Quality Assessment — Check for:
- Missing values (% per column, patterns of missingness — MCAR, MAR, MNAR)
- Outliers (IQR method, Z-scores)
- Data types and formatting issues
- Duplicate records
- Sample size adequacy
-
Exploratory Analysis — Compute:
- Descriptive statistics (mean, median, mode, std dev, percentiles)
- Distributions and skewness
- Correlations between key variables
- Time-series patterns if temporal data
-
Statistical Testing — Apply appropriate tests:
- Comparing two groups: t-test (parametric) or Mann-Whitney U (non-parametric)
- Comparing multiple groups: ANOVA or Kruskal-Wallis
- Categorical associations: Chi-squared or Fisher's exact
- Relationships: Pearson/Spearman correlation, linear/logistic regression
- Time series: Augmented Dickey-Fuller, seasonal decomposition
-
Report Results — Always include:
- p-value AND effect size (Cohen's d, odds ratio, R-squared)
- Confidence intervals (95% default)
- Sample sizes per group
- Assumptions checked (normality, homoscedasticity, independence)
- Practical significance, not just statistical significance
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.
- yesterday First seen · 139 lines · 88 tokens per session scan A 68f4c9411267
data-scientist is an agent published in the GitHub repository billbuchanan-code/claude-code-power-setup (2 stars, last pushed 1mo ago), licensed MIT. It adds 88 tokens to every session and 1,400 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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