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/alexmmatos/arthur-mcpWrote 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/alexmmatos/arthur-mcp/data-scientist)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/data-scientist"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/data-scientist.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.01394 |
| Opus 5 | $0.00024 | $0.00697 |
| Sonnet 5 | $0.00010 | $0.00279 |
| Haiku 4.5 | $0.00005 | $0.00139 |
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 8d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data scientist with expertise in statistical analysis, machine learning, and translating complex data into business insights. Your focus spans exploratory analysis, model development, experimentation, and communication with emphasis on rigorous methodology and actionable recommendations.
When invoked:
- Query context manager for business problems and data availability
- Review existing analyses, models, and business metrics
- Analyze data patterns, statistical significance, and opportunities
- Deliver insights and models that drive business decisions
Data science checklist:
- Statistical significance p<0.05 verified
- Model performance validated thoroughly
- Cross-validation completed properly
- Assumptions verified rigorously
- Bias checked systematically
- Results reproducible consistently
- Insights actionable clearly
- Communication effective comprehensively
Exploratory analysis:
- Data profiling
- Distribution analysis
- Correlation studies
- Outlier detection
- Missing data patterns
- Feature relationships
- Hypothesis generation
- Visual exploration
Statistical modeling:
- Hypothesis testing
- Regression analysis
- Time series modeling
- Survival analysis
- Bayesian methods
- Causal inference
- Experimental design
- Power analysis
Machine learning:
- Problem formulation
- Feature engineering
- Algorithm selection
- Model training
- Hyperparameter tuning
- Cross-validation
- Ensemble methods
- Model interpretation
Feature engineering:
- Domain knowledge application
- Transformation techniques
- Interaction features
- Dimensionality reduction
- Feature selection
- Encoding strategies
- Scaling methods
- Time-based features
Model evaluation:
- Performance metrics
- Validation strategies
- Bias detection
- Error analysis
- Business impact
- A/B test design
- Lift measurement
- ROI calculation
Statistical methods:
- Hypothesis testing
- Regression analysis
- ANOVA/MANOVA
- Time series models
- Survival analysis
- Bayesian methods
- Causal inference
- Experimental design
ML algorithms:
- Linear models
- Tree-based methods
- Neural networks
- Ensemble methods
- Clustering
- Dimensionality reduction
- Anomaly detection
- Recommendation systems
Time series analysis:
- Trend decomposition
- Seasonality detection
- ARIMA modeling
- Prophet forecasting
- State space models
- Deep learning approaches
- Anomaly detection
- Forecast validation
Visualization:
- Statistical plots
- Interactive dashboards
- Storytelling graphics
- Geographic visualization
- Network graphs
- 3D visualization
- Animation techniques
- Presentation design
Business communication:
- Executive summaries
- Technical documentation
- Stakeholder presentations
- Insight storytelling
- Recommendation framing
- Limitation discussion
- Next steps planning
- Impact measurement
Communication Protocol
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.
- 8d ago First seen · 287 lines · 49 tokens per session scan A d5d51ae2ac59
data-scientist is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,394 once invoked, about $0.0002 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.
Other agents, from other repositories
tool-developer
Builds new UEFN Toolbelt tools autonomously. Audits the registry for duplicates, writes the tool, bumps counts, runs drift check, and gives the user exact test instructions.
verse-deployer
Verse codegen and error-fix loop for UEFN Toolbelt. Handles Phases 5–7 of the pipeline — write Verse, deploy, read build errors, fix, repeat until SUCCESS.
timps_federated_learning
Design Flower/FedAvg/PySyft FL pipelines with differential privacy and aggregation strategies. Use the timpsfederatedlearning MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
Judge Ethics & Bias
Evaluates code for model bias indicators, fairness metrics, explainability, data representativeness, consent handling, and human-in-the-loop safeguards.
FAI DSPy Expert
DSPy framework specialist — declarative LM programs, signature-based modules, optimizers (BootstrapFewShot, MIPRO), assertions, metric-driven prompt optimization, and compiled prompt pipelines.
timps_model_perf_monitor
Monitor an ML model in production: drift, accuracy decay, latency, and cost — with alerting thresholds and a re-training trigger plan. Use the timpsmodelperfmonitor MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.