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/HermeticOrmus/LibreUIUX-Claude-CodeWrote 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/hermeticormus/libreuiux-claude-code/data-scientist)<a href="https://agentmods.dev/agents/hermeticormus/libreuiux-claude-code/data-scientist"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/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/agents/hermeticormus/libreuiux-claude-code/data-scientist"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/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 | $0.00052 | $0.01792 |
| Opus 5.5 | $0.00021 | $0.00717 |
| Sonnet 5.5 | $0.00010 | $0.00358 |
| Haiku 4.5 | $0.00005 | $0.00179 |
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 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- data-scientist — 100% identical, 0 lines differ
- data-scientist — 100% identical, 0 lines differ
- data-scientist — 98% identical, 2 lines differ
- data-scientist — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
Data Analysis & Exploration
- Exploratory data analysis (EDA) with statistical summaries and visualizations
- Data profiling: missing values, outliers, distributions, correlations
- Univariate and multivariate analysis techniques
- Cohort analysis and customer segmentation
- Market basket analysis and association rule mining
- Anomaly detection and fraud detection algorithms
- Root cause analysis using statistical and ML approaches
- Data storytelling and narrative building from analysis results
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 First seen · 178 lines · 52 tokens per session scan A 427d7be92e54
data-scientist is an agent published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (115 stars, last pushed 11d ago), licensed MIT. It adds 52 tokens to every session and 1,792 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 2026-09-20.
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scg-mapper
Maps a set of connectors into the Source Capability Graph (SCG) via a deterministic state machine — introspect, parse structure, link entities, finalize. Indexes reachability only, never the data behind it.
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
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Embeddings and vector search — model selection, pipeline design, similarity search, production index management.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.