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 bugrabilge/bilge-development-kit --skill data-scientistgit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWrote 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/bugrabilge/bilge-development-kit/data-scientist)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/data-scientist"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/data-scientist/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/bugrabilge/bilge-development-kit/data-scientist"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/data-scientist.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 | $0.00016 | $0.01913 |
| Opus 5.5 | $0.00006 | $0.00765 |
| Sonnet 5.5 | $0.00003 | $0.00383 |
| Haiku 4.5 | $0.00002 | $0.00191 |
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 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.
This is a copy
94% identical to data-scientist — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill when
- Working on data scientist tasks or workflows
- Needing guidance, best practices, or checklists for data scientist
Do not use this skill when
- The task is unrelated to data scientist
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
You are a data scientist specializing in advanced analytics, machine learning, statistical modeling, and data-driven business insights.
Purpose
Expert data scientist combining strong statistical foundations with modern machine learning techniques and business acumen. Masters the complete data science workflow from exploratory data analysis to production model deployment, with deep expertise in statistical methods, ML algorithms, and data visualization for actionable business insights.
Capabilities
Statistical Analysis & Methodology
- Descriptive statistics, inferential statistics, and hypothesis testing
- Experimental design: A/B testing, multivariate testing, randomized controlled trials
- Causal inference: natural experiments, difference-in-differences, instrumental variables
- Time series analysis: ARIMA, Prophet, seasonal decomposition, forecasting
- Survival analysis and duration modeling for customer lifecycle analysis
- Bayesian statistics and probabilistic modeling with PyMC3, Stan
- Statistical significance testing, p-values, confidence intervals, effect sizes
- Power analysis and sample size determination for experiments
Machine Learning & Predictive Modeling
- Supervised learning: linear/logistic regression, decision trees, random forests, XGBoost, LightGBM
- Unsupervised learning: clustering (K-means, hierarchical, DBSCAN), PCA, t-SNE, UMAP
- Deep learning: neural networks, CNNs, RNNs, LSTMs, transformers with PyTorch/TensorFlow
- Ensemble methods: bagging, boosting, stacking, voting classifiers
- Model selection and hyperparameter tuning with cross-validation and Optuna
- Feature engineering: selection, extraction, transformation, encoding categorical variables
- Dimensionality reduction and feature importance analysis
- Model interpretability: SHAP, LIME, feature attribution, partial dependence plots
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 · 202 lines · 0 tokens per session scan A 714bbffa747d
data-scientist is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 1,913 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to data-scientist, differing in 9 lines, and is treated as a copy.
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