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 skills/glincker/claude-code-marketplace/data-engineernpx skills add glincker/claude-code-marketplace --skill data-engineergit clone --depth 1 https://github.com/glincker/claude-code-marketplaceWrote 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/glincker/claude-code-marketplace/data-engineer)<a href="https://agentmods.dev/skills/glincker/claude-code-marketplace/data-engineer"><img src="https://agentmods.dev/badge/skills/glincker/claude-code-marketplace/data-engineer.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00295 |
| Opus 5 | $0.00009 | $0.00148 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
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 4d 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
Data Engineer Agent
Data engineering specialist agent for building ETL pipelines, data warehouses, and analytics infrastructure.
Agent Expertise
- ETL pipeline design (Apache Airflow, Dagster)
- Data warehousing (Snowflake, BigQuery, Redshift)
- Stream processing (Apache Kafka, Flink)
- Data modeling and schema design
- SQL optimization and query tuning
- Data quality and validation
Key Capabilities
- ETL Pipelines: Extract, transform, load workflows
- Data Warehousing: Star/snowflake schema design
- Real-time Processing: Streaming data pipelines
- Data Quality: Validation rules, anomaly detection
- Analytics: SQL queries, aggregations, reporting
Quick Commands
- "Design ETL pipeline for customer data"
- "Create data warehouse schema"
- "Build real-time analytics dashboard"
- "Optimize slow SQL queries"
- "Set up data quality checks"
Author
GLINCKER Team
What ships with it
1 file 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.
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.
- 4d ago First seen · 43 lines · 18 tokens per session scan A 448f0bb02942
data-engineer is a skill published in the GitHub repository glincker/claude-code-marketplace (36 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 295 once invoked, about $0.0001 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.
Other skills, from other repositories
polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
data-parity
Validate that two tables or query results are identical — or diagnose exactly how they differ. Discover schema, identify keys, profile cheaply, then diff. Use for migration validation, ETL regression, and query refactor verification.
dbt-develop
REQUIRED before writing or modifying ANY dbt model. Invoke this skill FIRST whenever a task says "create", "build", "add", "modify", "update", "fix", or "refactor" a dbt model, staging file, mart, incremental, or snapshot. Skipping this skill is the leading cause of silent-correctness bugs — models that compile and…
data-viz
Build modern, interactive data visualizations and dashboards using code-based component libraries (shadcn/ui, Recharts, Tremor, Nivo, D3, Victory, visx). Use this skill whenever the user asks to visualize data, build dashboards, create analytics views, chart metrics, tell a data story, build a reporting interface…
dbt-troubleshoot
Debug dbt errors — compilation failures, runtime database errors, test failures, wrong data, and performance issues. Use when something is broken, producing wrong results, or failing to build. Powered by altimate-dbt.