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 skills add xuansenpa1/skillrevise --skill senior-data-engineergit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/skills/xuansenpa1/skillrevise/senior-data-engineer)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/senior-data-engineer"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/senior-data-engineer/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/skills/xuansenpa1/skillrevise/senior-data-engineer"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/senior-data-engineer.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 | $0.00100 | $0.05130 |
| Opus 5 | $0.00050 | $0.02565 |
| Sonnet 5 | $0.00020 | $0.01026 |
| Haiku 4.5 | $0.00010 | $0.00513 |
Grade A, and why
senior-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 5d 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
98% identical to senior-data-engineer — 5 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 — 609 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Data Engineer
Core Capabilities
- Batch Pipeline Orchestration - Design and implement production-ready ETL/ELT pipelines with Airflow, intelligent dependency resolution, retry logic, and comprehensive monitoring
- Real-Time Streaming - Build event-driven streaming pipelines with Kafka, Flink, Kinesis, and Spark Streaming with exactly-once semantics and sub-second latency
- Data Quality Management - Comprehensive batch and streaming data quality validation covering completeness, accuracy, consistency, timeliness, and validity
- Streaming Quality Monitoring - Track consumer lag, data freshness, schema drift, throughput, and dead letter queue rates for streaming pipelines
- Performance Optimization - Analyze and optimize pipeline performance with query optimization, Spark tuning, and cost analysis recommendations
Key Workflows
Workflow 1: Build ETL Pipeline
Time: 2-4 hours
Steps:
- Design pipeline architecture using Lambda, Kappa, or Medallion pattern
- Configure YAML pipeline definition with sources, transformations, targets
- Generate Airflow DAG with
pipeline_orchestrator.py - Define data quality validation rules
- Deploy and configure monitoring/alerting
Expected Output: Production-ready ETL pipeline with 99%+ success rate, automated quality checks, and comprehensive monitoring
Workflow 2: Build Real-Time Streaming Pipeline
Time: 3-5 days
Steps:
- Select streaming architecture (Kappa vs Lambda) based on requirements
- Configure streaming pipeline YAML (sources, processing, sinks, quality)
- Generate Kafka configurations with
kafka_config_generator.py - Generate Flink/Spark job scaffolding with
stream_processor.py - Deploy and monitor with
streaming_quality_validator.py
Expected Output: Streaming pipeline processing 10K+ events/sec with P99 latency < 1s, exactly-once delivery, and real-time quality monitoring
World-class data engineering for production-grade data systems, scalable pipelines, and enterprise data platforms.
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/.gitkeep 0 B
- assets/data-quality-checklist.md 0 B
- assets/pipeline-design-template.md 0 B
- HOW_TO_USE.md 2.4 KB
- references/frameworks.md 41 KB
- references/templates.md 66 KB
- references/tools.md 38 KB
- scripts/data_quality_validator.py 3.4 KB runs code
- scripts/etl_performance_optimizer.py 3.4 KB runs code
- scripts/kafka_config_generator.py 40 KB runs code
- scripts/pipeline_orchestrator.py 3.4 KB runs code
- scripts/stream_processor.py 59 KB runs code
- scripts/streaming_quality_validator.py 39 KB runs code
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.
- 5d ago First seen · 609 lines · 100 tokens per session scan A dace177bca27
senior-data-engineer is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 3d ago), licensed MIT. It adds 100 tokens to every session and 5,130 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to senior-data-engineer, differing in 5 lines, and is treated as a copy.
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