setting-up-astro-project
Skill459 starsChanged 36 days agoApache-2.0View on GitHub
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
- Installs packages
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- miptah21/skillsedited, first seen May 26, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- miptah21/skills★ 299% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository35 other files it gives its agents.
- AGENTS.mdAGENTS.md
- astro-airflow-mcp/AGENTS.mdAGENTS.md
- airflow-hitlSkill
- airflow-pluginsSkill
- airflowSkill
- airflow-state-storeSkill
- analyzing-dataSkill
- annotating-task-lineageSkill
- authoring-dagsSkill
- authoring-go-sdk-tasksSkill
- authoring-java-sdk-tasksSkill
- authoring-language-sdk-tasksSkill
- blueprintSkill
- checking-freshnessSkill
- configuring-airflow-language-sdksSkill
- cosmos-dbt-coreSkill
- cosmos-dbt-fusionSkill
- creating-openlineage-extractorsSkill
- dag-factorySkill
- debugging-dagsSkill
- delegating-to-ottoSkill
- deploying-airflowSkill
- deploying-go-sdk-bundlesSkill
- deploying-java-sdk-bundlesSkill
- managing-astro-deploymentsSkill
- managing-astro-local-envSkill
- migrating-airflow-2-to-3Skill
- migrating-ai-sdk-to-common-aiSkill
- migrating-dagster-to-airflowSkill
- profiling-tablesSkill
- testing-dagsSkill
- tracing-downstream-lineageSkill
- tracing-upstream-lineageSkill
- troubleshooting-astro-deploymentsSkill
- warehouse-initSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 66
After modifying dependencies: Run astro dev restart
- Distinctive: line 120
managing-astro-local-env: Start, stop, and troubleshoot the local environment
- Distinctive: line 121
authoring-dags: Write and validate DAGs (uses MCP tools)
- Distinctive: line 122
testing-dags: Test DAGs (uses MCP tools)
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Astro Project Setup
This skill helps you initialize and configure Airflow projects using the Astro CLI.
To run the local environment, see the managing-astro-local-env skill. To write DAGs, see the authoring-dags skill. Open-source alternative: If the user isn't on Astro, guide them to Apache Airflow's Docker Compose quickstart for local dev and the Helm chart for production. For deployment strategies, use the
deploying-airflowskill.
Initialize a New Project
astro dev initDon't pass
--airflow-versionor--runtime-versionunless the user explicitly asks for a specific pin. Plainastro dev initresolves to the latest Astro Runtime — that's the right default. Specifying a version risks pinning to a stale value from training data. If the user wants to know what was installed, read the generatedDockerfileafterward instead of guessing.
Creates this structure:
project/
├── dags/ # DAG files
├── include/ # SQL, configs, supporting files
├── plugins/ # Custom Airflow plugins
├── tests/ # Unit tests
├── Dockerfile # Image customization
├── packages.txt # OS-level packages
├── requirements.txt # Python packages
└── airflow_settings.yaml # Connections, variables, poolsAdding Dependencies
Python Packages (requirements.txt)
apache-airflow-providers-snowflake==5.3.0
pandas==2.1.0
requests>=2.28.0OS Packages (packages.txt)
gcc
libpq-devCustom Dockerfile
For complex setups (private PyPI, custom scripts):
FROM quay.io/astronomer/astro-runtime:12.4.0
RUN pip install --extra-index-url https://pypi.example.com/simple my-packageAfter modifying dependencies: Run astro dev restart
Configuring Connections & Variables
airflow_settings.yaml
Loaded automatically on environment start:
airflow:
connections:
- conn_id: my_postgres
conn_type: postgres
host: host.docker.internal
port: 5432
login: user
password: pass
schema: mydb
variables:
- variable_name: env
variable_value: dev
pools:
- pool_name: limited_pool
pool_slot: 5Export/Import
# Export from running environment
astro dev object export --connections --file connections.yaml
# Import to environment
astro dev object import --connections --file connections.yamlValidate Before Running
Parse DAGs to catch errors without starting the full environment:
astro dev parseRelated Skills
- managing-astro-local-env: Start, stop, and troubleshoot the local environment
- authoring-dags: Write and validate DAGs (uses MCP tools)
- testing-dags: Test DAGs (uses MCP tools)
- deploying-airflow: Deploy DAGs to production (Astro, Docker Compose, Kubernetes)
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

