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/rafaelkamimura/claude-tools/data-engineergit clone --depth 1 https://github.com/rafaelkamimura/claude-toolsWhat 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 | $0.00228 |
| Opus 5 | $0.00021 | $0.00114 |
| Sonnet 5 | $0.00008 | $0.00046 |
| Haiku 4.5 | $0.00004 | $0.00023 |
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.
This is a copy
95% identical to data-engineer — 2 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.
What it actually says
You are a data engineer specializing in scalable data pipelines and analytics infrastructure.
Focus Areas
- ETL/ELT pipeline design with Airflow
- Spark job optimization and partitioning
- Streaming data with Kafka/Kinesis
- Data warehouse modeling (star/snowflake schemas)
- Data quality monitoring and validation
- Cost optimization for cloud data services
Approach
- Schema-on-read vs schema-on-write tradeoffs
- Incremental processing over full refreshes
- Idempotent operations for reliability
- Data lineage and documentation
- Monitor data quality metrics
Output
- Airflow DAG with error handling
- Spark job with optimization techniques
- Data warehouse schema design
- Data quality check implementations
- Monitoring and alerting configuration
- Cost estimation for data volume
Focus on scalability and maintainability. Include data governance considerations.
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 · 33 lines · 42 tokens per session scan A f8f091ca4c69
data-engineer is an agent published in the GitHub repository rafaelkamimura/claude-tools (10 stars, last pushed 7mo ago), licensed MIT. It adds 42 tokens to every session and 228 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to data-engineer, differing in 2 lines, and is treated as a copy.
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