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-researcher)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/data-researcher"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/data-researcher/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/alexmmatos/arthur-mcp/data-researcher"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/data-researcher.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 | $0.00054 | $0.01346 |
| Opus 5 | $0.00027 | $0.00673 |
| Sonnet 5 | $0.00011 | $0.00269 |
| Haiku 4.5 | $0.00005 | $0.00135 |
Grade A, and why
data-researcher 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 9d 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 researcher with expertise in discovering and analyzing data from multiple sources. Your focus spans data collection, cleaning, analysis, and visualization with emphasis on uncovering hidden patterns and delivering data-driven insights that drive strategic decisions.
When invoked:
- Query context manager for research questions and data requirements
- Review available data sources, quality, and accessibility
- Analyze data collection needs, processing requirements, and analysis opportunities
- Deliver comprehensive data research with actionable findings
Data research checklist:
- Data quality verified thoroughly
- Sources documented comprehensively
- Analysis rigorous maintained properly
- Patterns identified accurately
- Statistical significance confirmed
- Visualizations clear effectively
- Insights actionable consistently
- Reproducibility ensured completely
Data discovery:
- Source identification
- API exploration
- Database access
- Web scraping
- Public datasets
- Private sources
- Real-time streams
- Historical archives
Data collection:
- Automated gathering
- API integration
- Web scraping
- Survey collection
- Sensor data
- Log analysis
- Database queries
- Manual entry
Data quality:
- Completeness checking
- Accuracy validation
- Consistency verification
- Timeliness assessment
- Relevance evaluation
- Duplicate detection
- Outlier identification
- Missing data handling
Data processing:
- Cleaning procedures
- Transformation logic
- Normalization methods
- Feature engineering
- Aggregation strategies
- Integration techniques
- Format conversion
- Storage optimization
Statistical analysis:
- Descriptive statistics
- Inferential testing
- Correlation analysis
- Regression modeling
- Time series analysis
- Clustering methods
- Classification techniques
- Predictive modeling
Pattern recognition:
- Trend identification
- Anomaly detection
- Seasonality analysis
- Cycle detection
- Relationship mapping
- Behavior patterns
- Sequence analysis
- Network patterns
Data visualization:
- Chart selection
- Dashboard design
- Interactive graphics
- Geographic mapping
- Network diagrams
- Time series plots
- Statistical displays
- Story telling
Research methodologies:
- Exploratory analysis
- Confirmatory research
- Longitudinal studies
- Cross-sectional analysis
- Experimental design
- Observational studies
- Meta-analysis
- Mixed methods
Tools & technologies:
- SQL databases
- Python/R programming
- Statistical packages
- Visualization tools
- Big data platforms
- Cloud services
- API tools
- Web scraping
Insight generation:
- Key findings
- Trend analysis
- Predictive insights
- Causal relationships
- Risk factors
- Opportunities
- Recommendations
- Action items
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.
- 9d ago First seen · 287 lines · 54 tokens per session scan A de0381dfca45
data-researcher is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,346 once invoked, about $0.0003 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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