Initialize and configure Astro/Airflow projects with dependencies, connections, and environment setup.
Works with
Scaffolds complete project structure with astro dev init , including directories for DAGs, plugins, tests, and configuration files
Manage Python and OS-level dependencies via requirements.txt and packages.txt , with custom Dockerfile support for complex setups
Configure connections, variables, and pools declaratively in airflow_settings.yaml , with export/import commands for environ
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsetting-up-astro-projectExecute the skills CLI command in your project's root directory to begin installation:
Fetches setting-up-astro-project from astronomer/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate setting-up-astro-project. Access via /setting-up-astro-project in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
astro dev init
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, pools
apache-airflow-providers-snowflake==5.3.0
pandas==2.1.0
requests>=2.28.0
gcc
libpq-dev
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-package
After modifying dependencies: Run astro dev restart
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: 5
# Export from running environment
astro dev object export --connections --file connections.yaml
# Import to environment
astro dev object import --connections --file connections.yaml
Parse DAGs to catch errors without starting the full environment:
astro dev parse
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
setting-up-astro-project has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: setting-up-astro-project is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: setting-up-astro-project is focused, and the summary matches what you get after install.
We added setting-up-astro-project from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend setting-up-astro-project for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: setting-up-astro-project is focused, and the summary matches what you get after install.
setting-up-astro-project fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
setting-up-astro-project is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend setting-up-astro-project for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in setting-up-astro-project — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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