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 curiositech/some_claude_skills --skill data-pipeline-engineergit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote 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/curiositech/some_claude_skills/data-pipeline-engineer)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/data-pipeline-engineer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/data-pipeline-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/curiositech/some_claude_skills/data-pipeline-engineer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/data-pipeline-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00087 | $0.01454 |
| Opus 5 | $0.00044 | $0.00727 |
| Sonnet 5 | $0.00017 | $0.00291 |
| Haiku 4.5 | $0.00009 | $0.00145 |
Grade A, and why
data-pipeline-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 10d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Engineer
Expert data engineer specializing in ETL/ELT pipelines, streaming architectures, data warehousing, and modern data stack implementation.
Quick Start
- Identify sources - data formats, volumes, freshness requirements
- Choose architecture - Medallion (Bronze/Silver/Gold), Lambda, or Kappa
- Design layers - staging → intermediate → marts (dbt pattern)
- Add quality gates - Great Expectations or dbt tests at each layer
- Orchestrate - Airflow DAGs with sensors and retries
- Monitor - lineage, freshness, anomaly detection
Core Capabilities
| Capability | Technologies | Key Patterns |
|---|---|---|
| Batch Processing | Spark, dbt, Databricks | Incremental, partitioning, Delta/Iceberg |
| Stream Processing | Kafka, Flink, Spark Streaming | Watermarks, exactly-once, windowing |
| Orchestration | Airflow, Dagster, Prefect | DAG design, sensors, task groups |
| Data Modeling | dbt, SQL | Kimball, Data Vault, SCD |
| Data Quality | Great Expectations, dbt tests | Validation suites, freshness |
Architecture Patterns
Medallion Architecture (Recommended)
BRONZE (Raw) → Exact source copy, schema-on-read, partitioned by ingestion
↓ Cleaning, Deduplication
SILVER (Cleansed) → Validated, standardized, business logic applied
↓ Aggregation, Enrichment
GOLD (Business) → Dimensional models, aggregates, ready for BI/ML
Lambda vs Kappa
- Lambda: Batch + Stream layers → merged serving layer (complex but complete)
- Kappa: Stream-only with replay → simpler but requires robust streaming
Reference Examples
Full implementation examples in ./references/:
| File | Description |
|---|---|
dbt-project-structure.md |
Complete dbt layout with staging, intermediate, marts |
airflow-dag.py |
Production DAG with sensors, task groups, quality checks |
spark-streaming.py |
Kafka-to-Delta processor with windowing |
great-expectations-suite.json |
Comprehensive data quality expectation suite |
What ships with it
7 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.
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.
- 10d ago First seen · 153 lines · 87 tokens per session scan A 940c86ebe78c
data-pipeline-engineer is a skill published in the GitHub repository curiositech/some_claude_skills (218 stars, last pushed 4d ago), licensed MIT. It adds 87 tokens to every session and 1,454 once invoked, about $0.0004 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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