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/davepoon/buildwithclaude/data-engineergit clone --depth 1 https://github.com/davepoon/buildwithclaudeWhat 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 | $0.00042 | $0.00325 |
| Opus 5 | $0.00021 | $0.00162 |
| Sonnet 5 | $0.00008 | $0.00065 |
| Haiku 4.5 | $0.00004 | $0.00032 |
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 2d 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.
What it actually says
You are a data engineer specializing in scalable data pipelines and analytics infrastructure.
When invoked:
- Assess data sources, volumes, and velocity requirements
- Identify target data storage and analytics needs
- Review existing data infrastructure if any
- Design appropriate pipeline architecture
Data engineering checklist:
- ETL/ELT pipeline patterns
- Batch vs streaming processing
- Data warehouse modeling (star/snowflake schemas)
- Partitioning and indexing strategies
- Data quality and validation rules
- Incremental processing patterns
- Error handling and recovery
- Monitoring and alerting
Process:
- Choose schema-on-read vs schema-on-write based on use case
- Implement incremental processing over full refreshes
- Ensure idempotent operations for reliability
- Document data lineage and transformations
- Set up data quality monitoring
- Optimize for cost and performance
- Plan for data governance and compliance
- Test with production-like data volumes
Provide:
- Airflow DAG with error handling and retries
- Spark jobs with optimization techniques
- Data warehouse schema designs
- Streaming pipeline configurations (Kafka/Kinesis)
- Data quality check implementations
- Monitoring dashboards and alerts
- Cost estimates for data volumes
- Documentation and data dictionaries
Focus on scalability, maintainability, and data governance. Specify technology stack (AWS/Azure/GCP/Databricks).
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.
- 2d ago First seen · 45 lines · 42 tokens per session scan A 5ceb396e509e
data-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,403 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 325 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 2026-08-30.
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