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 j4flmao/agent-skills --skill etl-pipelinegit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/etl-pipeline)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/etl-pipeline"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/etl-pipeline/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/j4flmao/agent-skills/etl-pipeline"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/etl-pipeline.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 | $0.00100 | $0.05120 |
| Opus 5 | $0.00050 | $0.02560 |
| Sonnet 5 | $0.00020 | $0.01024 |
| Haiku 4.5 | $0.00010 | $0.00512 |
Grade A, and why
data-etl-pipeline 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 8d 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 — 567 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data ETL Pipeline
Purpose
Design reliable ETL/ELT pipelines with Airflow orchestration, dbt transformations, incremental strategies, error handling, and data validation.
Agent Protocol
Trigger
Exact user phrases: "ETL", "ELT", "data pipeline", "Airflow", "dbt", "data transformation", "data ingestion", "batch processing", "pipeline orchestration", "incremental load", "data pipeline design", "DAG", "data workflow", "extract load transform".
Input Context
Before activating, verify:
- Source systems (databases, APIs, files, streams)
- Target warehouse (Snowflake, BigQuery, Redshift, DuckDB)
- Volume and frequency (daily/hourly batch, CDC, real-time)
- Orchestration preference (Airflow, Dagster, Prefect)
- Transformation tool (dbt, custom SQL, Spark)
- Data volume and growth rate
- SLAs for data freshness and availability
- Existing monitoring and alerting infrastructure
Output Artifact
ETL pipeline design with DAG structure, transformation config, error handling as YAML and SQL.
Response Format
# Airflow DAG skeleton
# Task definitions
# dbt model config
# Incremental strategy
# Transformation query template
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Pipeline architecture diagram defined (sources → staging → warehouse)
- Airflow DAG structure with task dependencies and retries
- Incremental loading strategy selected and configured
- Error handling with retry, dead-letter, and notification
- Data validation checks on each stage
- Monitoring and alerting configured
- Data lineage tracking set up
Max Response Length
300 lines of code and configuration.
ETL vs ELT
ETL (Extract, Transform, Load)
Transform happens before loading. Best for: on-premises databases, structured data, complex transformations requiring significant compute, regulatory environments requiring data masking before storage. ETL requires a transformation engine (Spark, Python) between extraction and loading. Transformation reduces data volume before warehouse storage, saving on warehouse costs.
What ships with it
6 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.
- 8d ago First seen · 567 lines · 100 tokens per session scan A b74816801cbc
data-etl-pipeline is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 5d ago), licensed MIT. It adds 100 tokens to every session and 5,120 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
arrowspace
Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
docetl
Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.
malloy-model
Build Malloy semantic models with base source and joined source files. Use when creating or modifying .malloy files, user asks to "create a malloy model", "add dimensions", "add measures", "create a source", or any Malloy model authoring task.