clari-deploy-integration
Deploy Clari export pipelines to production with Airflow, Cloud Functions, or Lambda. Use when scheduling automated exports, deploying to cloud platforms, or setting up serverless Clari sync. Trigger with phrases like "deploy clari", "clari airflow", "clari lambda", "clari cloud function", "clari scheduled export". '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill clari-deploy-integration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Clari Deploy Integration ## Overview Deploy Clari export pipelines to production environments: Airflow DAGs, AWS Lambda, or Google Cloud Functions for scheduled, serverless execution. ## Instructions ### Airflow DAG ```python # dags/clari_export_dag.py from airflow import DAG from airflow.operators.python import PythonOperator from airflow.models import Variable from datetime import datetime, ti
What does the clari-deploy-integration skill do?
Deploy Clari export pipelines to production with Airflow, Cloud Functions, or Lambda. Use when scheduling automated exports, deploying to cloud platforms, or setting up serverless Clari sync. Trigger with phrases like "deploy clari", "clari airflow", "clari lambda", "clari cloud function", "clari scheduled export". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill clari-deploy-integration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
