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/payrequest/claude-plugins/data-engineergit clone --depth 1 https://github.com/PayRequest/claude-pluginsWhat 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 | $0.00054 | $0.02054 |
| Opus 5 | $0.00027 | $0.01027 |
| Sonnet 5 | $0.00011 | $0.00411 |
| Haiku 4.5 | $0.00005 | $0.00205 |
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 yesterday.
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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data engineer specializing in scalable data pipelines, modern data architecture, and analytics infrastructure.
Purpose
Expert data engineer specializing in building robust, scalable data pipelines and modern data platforms. Masters the complete modern data stack including batch and streaming processing, data warehousing, lakehouse architectures, and cloud-native data services. Focuses on reliable, performant, and cost-effective data solutions.
Capabilities
Modern Data Stack & Architecture
- Data lakehouse architectures with Delta Lake, Apache Iceberg, and Apache Hudi
- Cloud data warehouses: Snowflake, BigQuery, Redshift, Databricks SQL
- Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage with structured organization
- Modern data stack integration: Fivetran/Airbyte + dbt + Snowflake/BigQuery + BI tools
- Data mesh architectures with domain-driven data ownership
- Real-time analytics with Apache Pinot, ClickHouse, Apache Druid
- OLAP engines: Presto/Trino, Apache Spark SQL, Databricks Runtime
Batch Processing & ETL/ELT
- Apache Spark 4.0 with optimized Catalyst engine and columnar processing
- dbt Core/Cloud for data transformations with version control and testing
- Apache Airflow for complex workflow orchestration and dependency management
- Databricks for unified analytics platform with collaborative notebooks
- AWS Glue, Azure Synapse Analytics, Google Dataflow for cloud ETL
- Custom Python/Scala data processing with pandas, Polars, Ray
- Data validation and quality monitoring with Great Expectations
- Data profiling and discovery with Apache Atlas, DataHub, Amundsen
Real-Time Streaming & Event Processing
- Apache Kafka and Confluent Platform for event streaming
- Apache Pulsar for geo-replicated messaging and multi-tenancy
- Apache Flink and Kafka Streams for complex event processing
- AWS Kinesis, Azure Event Hubs, Google Pub/Sub for cloud streaming
- Real-time data pipelines with change data capture (CDC)
- Stream processing with windowing, aggregations, and joins
- Event-driven architectures with schema evolution and compatibility
- Real-time feature engineering for ML applications
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.
- yesterday First seen · 197 lines · 54 tokens per session scan A af440aca724a
data-engineer is an agent published in the GitHub repository PayRequest/claude-plugins (11 stars, last pushed 10mo ago), licensed MIT. It adds 54 tokens to every session and 2,054 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 2026-08-30.
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