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Tool guide
Apache Airflow
Defines source-backed Apache Airflow DAG design, scheduling, credentials, testing, deployment, and operations, evidence handling, and action boundaries.
Read the fileOverview
Apache Airflow overview
Use this bundle to prepare source-backed Apache Airflow DAG design, scheduling, credentials, testing, deployment, and operations and a review-ready Airflow workflow change brief.
Read the fileWorkflow
Apache Airflow source-backed triage
1. State the requested decision or artifact. 2. Inventory evidence: Airflow and provider versions, deployment, executor, scheduler, workers, and environment; DAG code, owners, tasks.
Read the fileTemplate
Airflow workflow change brief
Review-ready artifact for Apache Airflow DAG design, scheduling, credentials, testing, deployment, and operations, evidence quality, verification, and controlled actions.
Read the fileIs this bundle right for your task?
Who it is for
- Data engineers, analytics engineers, platform teams, DAG owners, and operational reviewers planning or investigating Apache Airflow workflows
When to use it
- A DAG schedule, timezone, data interval, catchup, retry, pool, concurrency, callback, or dependency change needs impact reviewed before deployment.
- A parse, task, data-completeness, backfill, or incident question requires DAG code, logs, run history, environment, and source definitions reconciled.
- A production workflow change needs tests, data-contract checks, alternatives, rollback, stop conditions, ownership, and approval boundaries.
What you need to provide
- Airflow and provider versions, deployment and executor context, scheduler and worker environment, DAG code, owners, tasks, dependencies, schedule, timezone, data interval, catchup, retries, pools, concurrency, callbacks, and assets.
- Connection and variable metadata without exposed secrets, secrets-backend and permission context, tests, run history, logs, data contracts, proposed change, backfill scope, rollback evidence, and approvals.
Tasks and expected outputs
Questions it helps answer
- Prepare an Airflow workflow change brief without fabricating local facts.
- Separate verified, provided, assumed, and missing evidence.
- Produce a review-ready decision with explicit verification and approval boundaries.
What it helps produce
- Airflow workflow change brief
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Load the Apache Airflow bundle and provide the Airflow and provider versions, executor and environment, DAG code and ownership, schedule and timezone, task dependencies, retry and pool settings, connection metadata, test results, run history, logs, data contracts, proposed backfill scope, and rollback plan. Ask the agent to reconcile schedule semantics and draft a workflow change brief without triggering or deploying the DAG.
Context path: bundles/tools/apache-airflow
What the bundle includes
Tools
- Apache Airflow
Frameworks
- source-evidence matrix
- Apache Airflow DAG design, scheduling, credentials, testing, deployment, and operations review matrix
- qualified-review gate
Evaluations
- Apache Airflow source verification check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Inventing DAG parse state, schedule behavior, credential validity, task outcome, data completeness, backfill impact, production state, or incident cause; deploying code, exposing connections, triggering or unpausing DAGs, backfilling, clearing tasks, altering pools, or moving production data without approval.
Known limitations
- Use the listed authoritative sources for general role or tool behavior; local configuration, records, values, states, permissions, and results require inspected evidence.
- Task-specific work requires current evidence for Airflow and provider versions, deployment, executor, scheduler, workers, and environment; DAG code, owners, tasks, dependencies, schedule, timezone, data interval, catchup, retries, pools, concurrency, callbacks, assets, connections, variables, secrets backend, permissions, tests, run history, logs, data contracts, rollback, and approval.
- Do not infer DAG parse state, schedule behavior, credential validity, task outcome, data completeness, backfill impact, production state, or incident cause.
Safety notes
- Minimize personal, customer, employee, financial, credential, security, privileged, health, student, and other sensitive data.
- Require explicit confirmation before actions that deploy DAG code, create or expose a connection, unpause or trigger a DAG, backfill, clear tasks, alter pools, or move production data.
- Route legal, privacy, security, compliance, financial, employment, clinical, safety, and other qualified judgments to an evidenced accountable reviewer.