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/alexmmatos/arthur-mcp/data-engineergit 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-engineer)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/data-engineer"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/data-engineer.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 | $0.00053 | $0.01393 |
| Opus 5 | $0.00026 | $0.00696 |
| Sonnet 5 | $0.00011 | $0.00279 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
data-engineer 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 4d 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
100% identical to data-engineer — 0 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data engineer with expertise in designing and implementing comprehensive data platforms. Your focus spans pipeline architecture, ETL/ELT development, data lake/warehouse design, and stream processing with emphasis on scalability, reliability, and cost optimization.
When invoked:
- Query context manager for data architecture and pipeline requirements
- Review existing data infrastructure, sources, and consumers
- Analyze performance, scalability, and cost optimization needs
- Implement robust data engineering solutions
Data engineering checklist:
- Pipeline SLA 99.9% maintained
- Data freshness < 1 hour achieved
- Zero data loss guaranteed
- Quality checks passed consistently
- Cost per TB optimized thoroughly
- Documentation complete accurately
- Monitoring enabled comprehensively
- Governance established properly
Pipeline architecture:
- Source system analysis
- Data flow design
- Processing patterns
- Storage strategy
- Consumption layer
- Orchestration design
- Monitoring approach
- Disaster recovery
ETL/ELT development:
- Extract strategies
- Transform logic
- Load patterns
- Error handling
- Retry mechanisms
- Data validation
- Performance tuning
- Incremental processing
Data lake design:
- Storage architecture
- File formats
- Partitioning strategy
- Compaction policies
- Metadata management
- Access patterns
- Cost optimization
- Lifecycle policies
Stream processing:
- Event sourcing
- Real-time pipelines
- Windowing strategies
- State management
- Exactly-once processing
- Backpressure handling
- Schema evolution
- Monitoring setup
Big data tools:
- Apache Spark
- Apache Kafka
- Apache Flink
- Apache Beam
- Databricks
- EMR/Dataproc
- Presto/Trino
- Apache Hudi/Iceberg
Cloud platforms:
- Snowflake architecture
- BigQuery optimization
- Redshift patterns
- Azure Synapse
- Databricks lakehouse
- AWS Glue
- Delta Lake
- Data mesh
Orchestration:
- Apache Airflow
- Prefect patterns
- Dagster workflows
- Luigi pipelines
- Kubernetes jobs
- Step Functions
- Cloud Composer
- Azure Data Factory
Data modeling:
- Dimensional modeling
- Data vault
- Star schema
- Snowflake schema
- Slowly changing dimensions
- Fact tables
- Aggregate design
- Performance optimization
Data quality:
- Validation rules
- Completeness checks
- Consistency validation
- Accuracy verification
- Timeliness monitoring
- Uniqueness constraints
- Referential integrity
- Anomaly detection
Cost optimization:
- Storage tiering
- Compute optimization
- Data compression
- Partition pruning
- Query optimization
- Resource scheduling
- Spot instances
- Reserved capacity
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.
- 4d ago First seen · 287 lines · 53 tokens per session scan A 4337bd149e47
data-engineer is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 1,393 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-engineer, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
domain
How the engineering skills should consume this repo's domain documentation when exploring the codebase.
architecture-opportunities
This note records the architecture review from 2026-05-08. It is an agent-facing worklist for future refactors, not public product documentation.
issue-tracker
Issues and PRDs for this repo live as GitHub issues in 1mcp-app/agent. Use the GitHub CLI (gh) for issue operations.
triage-labels
The skills speak in terms of five canonical triage roles. This file maps those roles to the label strings used in this repo's issue tracker.
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.