Guide · Agent skill
How to use the Data Pipeline Expert agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The data-pipeline skill is a set of written data engineering guidelines that an AI agent follows when you ask it about ETL, ELT, Apache Airflow, Apache Spark, dbt or data quality. It is aimed at data engineers and analytics engineers who want consistent, reliable advice on pipeline design. After this guide you will know what the skill changes about the agent's answers, how to install it, how to trigger it, and what to check before trusting its output.
Data Pipeline ExpertRightNow-AI/openfang · Apache-2.0 Download the skill Quick start
- 1Download the ZIP. Get the data-pipeline ZIP from the skill's page in the directory.
- 2Unzip the skill. Extract the archive so you have a folder containing SKILL.md.
- 3Place it in the right folder. Move the folder to ~/.claude/skills/ for personal use, or to .claude/skills/ in a project.
- 4Or upload it in the Claude apps. Go to Settings → Capabilities → Skills and upload the skill there instead.
- 5Describe your pipeline task. Ask in plain language, naming the tool (Airflow, Spark, dbt) and your situation.
- 6Review the output. Check generated code and designs against your data and environment before using them.
What the Data Pipeline Expert skill makes the agent do
Once installed, the skill tells the agent to apply a fixed set of data engineering principles when you ask for pipeline help. The source covers ELT versus ETL, idempotent steps, partitioning by time or logical keys, data quality checks between stages, and keeping orchestration separate from computation.
It also gives specific guidance for Airflow DAGs (task-level retries, timeouts, SLAs, sensors and XCom), Spark jobs (repartition/coalesce, broadcast joins for small dimension tables, caching), dbt projects (staging, intermediate and mart models, and schema, relationship and custom data tests) and data quality gates using frameworks like Great Expectations.
- Common patterns: incremental load, backfill strategy, dead letter queue and schema evolution
- Pitfalls: heavy computation inside Airflow operators, skipping validation after ingestion, hardcoded credentials, and unnecessary full table scans
Install the skill from a ZIP file
The skill ships as a single file, SKILL.md. Download the ZIP from the skill's page in the directory, then unzip it. You can install it for yourself or for one project.
For a personal install, place the skill folder in ~/.claude/skills/. For a project install, place it in the project's .claude/skills/ folder so it applies only to that codebase.
In the Claude apps you can instead upload the skill under Settings → Capabilities → Skills.
- Personal: ~/.claude/skills/ (the folder contains SKILL.md)
- Project: .claude/skills/ inside your project folder
- Claude apps: Settings → Capabilities → Skills, then upload
Trigger the skill with natural requests
You do not need a special command. Describe a data pipeline task in plain language that mentions the relevant area, such as Airflow, Spark, dbt, ETL or data quality. The agent will use the skill's guidance when the request matches its description.
Be specific about your situation. Include the tool, the data volume, how often the pipeline runs, and any failure you are seeing. Specific requests get answers that fit your design rather than generic advice.
- Review my Airflow DAG for retries, timeouts and backfill safety. Here is the file: ...
- Structure a dbt project for orders with staging, intermediate and mart models.
- How should I handle bad records in a Spark ingestion job without stopping the run?
- Design an incremental load using updated_at as the high-watermark column.
Examples of what to ask and what you get
Ask for a design and the agent answers using the patterns in the skill. For example, asking how to rerun last month's data should lead to a date-parameterized DAG, so backfills use the same code path as daily runs. Asking why a load keeps duplicating rows should lead to a check that each step is idempotent.
Ask for a review and the agent checks your code against the same principles. Paste the DAG, dbt model or Spark job and say what you want checked. The skill is most useful as a checklist for reviewers.
What you need and what the skill does not do
The source lists no required tools, packages or accounts. It does not run scripts and does not use the network. The skill is only text guidance: the agent cannot connect to your databases, warehouses or schedulers, and it cannot confirm that code it writes actually works.
Treat all output as a draft for human review, especially SQL, Spark code and Airflow configuration that will touch production data.
- Requires: nothing listed
- Runs scripts: no
- Uses network: no
- Files: SKILL.md
Safety notes and tips
Because the skill runs no scripts and makes no network calls, installing it does not give the agent access to your systems. Access still depends on whatever tools and permissions the agent already has in your environment. Do not paste live credentials into your requests; the skill itself advises using secrets managers and environment-based configuration.
For better results, give the agent your real constraints, such as warehouse type, data freshness needs and existing orchestration. Ask it to explain its reasoning so you can check each recommendation.
- Do not paste passwords, API keys or connection strings into prompts
- Test generated pipelines in a non-production environment first
- Ask for the trade-offs of each option, not only the recommendation
What people use it for
- Reviewing an Airflow DAG for retries, timeouts, SLAs and safe backfills
- Structuring a dbt project into staging, intermediate and mart models with tests
- Designing an incremental load using a high-watermark column
- Planning how to route failed records to a dead letter table or topic
- Checking a Spark job for partitioning, broadcast joins and caching choices
Frequently asked questions
How do I install the data-pipeline agent skill?
Download the ZIP from the skill's page, unzip it, and place the folder in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder. In the Claude apps, you can also upload it under Settings → Capabilities → Skills.
Does the data-pipeline skill require anything else?
The source lists no requirements. The skill is a single SKILL.md file that runs no scripts and uses no network access.
Is the data-pipeline skill free?
The source does not state pricing. The project is listed under the Apache-2.0 licence, so check that licence for terms of use.
Can the skill run my Airflow or Spark pipelines?
No. The skill contains only text guidance. The agent cannot connect to your systems or verify that your code works, so outputs need human review.
What is the difference between ETL and ELT in this skill?
The skill recommends ELT when your target warehouse can handle transformations: load raw data first, then transform it in place for reproducibility and auditability. It notes that ETL may fit other setups.
Who should not use this skill?
Non-technical users looking for a finished data tool or dashboard will not get that from it. It is for people who already build or review pipelines and want consistent guidance.
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Our verdict
This skill suits data engineers and analytics engineers who want consistent, checklist-style advice on idempotent pipelines, Airflow and Spark design, and dbt project structure. It is not a tool that runs or deploys pipelines, so pick a different option if you need automated execution or a finished data product.
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