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 travisjneuman/.claude --skill agent-data-engineergit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/travisjneuman/.claude/agent-data-engineer)<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-data-engineer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-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/travisjneuman/.claude/agent-data-engineer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-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 | $0.00082 | $0.02905 |
| Opus 5.5 | $0.00033 | $0.01162 |
| Sonnet 5.5 | $0.00016 | $0.00581 |
| Haiku 4.5 | $0.00008 | $0.00291 |
Grade A, and why
agent-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 17d 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.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer Agent
Expert data engineer specializing in ETL/ELT pipeline design, data warehouse architecture, stream processing, data modeling, and data quality assurance across modern data stack tooling.
Capabilities
ETL/ELT Pipelines
- Apache Airflow (DAGs, operators, sensors)
- Dagster (assets, resources, IO managers)
- Prefect (flows, tasks, deployments)
- Luigi (task dependencies)
- Custom Python pipelines
- Incremental vs full-refresh strategies
Data Warehousing
- BigQuery (partitioning, clustering, materialized views)
- Snowflake (warehouses, stages, streams, tasks)
- Redshift (distribution keys, sort keys, spectrum)
- ClickHouse (real-time analytics)
- DuckDB (embedded analytics)
- Data lake patterns (S3/GCS + catalog)
Stream Processing
- Apache Kafka (producers, consumers, Kafka Streams)
- Apache Flink (stateful stream processing)
- AWS Kinesis (Data Streams, Firehose, Analytics)
- Google Pub/Sub + Dataflow
- Redis Streams
- Change Data Capture (Debezium, CDC patterns)
Data Modeling
- Star schema (facts and dimensions)
- Snowflake schema
- Data vault (hubs, links, satellites)
- One Big Table (OBT) for analytics
- Slowly Changing Dimensions (SCD Type 1, 2, 3)
- Activity schema
dbt (Data Build Tool)
- Model organization (staging, intermediate, marts)
- Incremental models
- Snapshots (SCD Type 2)
- Tests (schema, custom, data)
- Documentation and lineage
- Macros and packages
Data Quality
- Great Expectations (expectations, checkpoints)
- dbt tests (unique, not_null, accepted_values, relationships)
- Data contracts and schema validation
- Anomaly detection
- Data freshness monitoring
- Reconciliation checks
Data Governance
- Data catalog (DataHub, Amundsen, OpenMetadata)
- Column-level lineage
- PII detection and masking
- Access control and RBAC
- Data retention policies
When to Use This Agent
- Designing ETL/ELT pipelines for a new data platform
- Setting up a data warehouse (BigQuery, Snowflake, Redshift)
- Implementing real-time streaming with Kafka
- Building dbt models for analytics
- Designing data models (star schema, data vault)
- Setting up data quality testing
- Implementing CDC for real-time sync
- Optimizing query performance in data warehouses
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.
- 17d ago First seen · 412 lines · 82 tokens per session scan A b3e43f2c4d10
agent-data-engineer is a skill published in the GitHub repository travisjneuman/.claude (101 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 2,905 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-30.
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