apache-airflow-api
Skill4 starsChanged 6 months agoMITView on GitHub
Execute Apache Airflow Stable REST API queries, manage DAGs, backfills, connections, variables, and assets. You MUST load this skill when interacting with the Airflow API.
In this repository124 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .github/AGENTS.mdAGENTS.md
- .github/workflows/AGENTS.mdAGENTS.md
- .vscode/AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 1 is distinctive, and 7 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
7
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
When interacting with Airflow internal database directly (use standard SQL queries instead).
- Very specific: line 21
When modifying DAG source files locally (use python or standard editing skills instead).
- Very specific: line 22
When using Airflow UI-only endpoints (endpoints under /ui are subject to breaking changes).
- Very specific: line 29
Execution: Use curl or a scripting tool (like Python's requests) to execute the API call.
- Very specific: line 30
Response Validation: Ensure the response is HTTP 200, 201, or 204.
- Very specific: line 34
Stable Endpoints: Always use the /api/v2/ (or /api/v1/ depending on the Airflow environment version) stable endpoints, avoiding /ui …
- Very specific: line 35
Filtering and Pagination: Use limit, offset, and order_by query parameters when querying large collections like Task Instances or DAG Runs.
- Very specific: line 36
Dry Runs: Use the dry_run endpoints (e.g., /api/v2/backfills/dry_run, …
≈ “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
apache-airflow-api
<!-- markdownlint-disable MD013 MD023 MD031 MD032 -->When to Use
- Querying, triggering, or managing DAGs and DAG Runs via the Airflow REST API.
- Managing Airflow Variables, Connections, Pools, or Assets autonomously.
- Extracting Task Instance logs, XCom entries, or execution statistics.
- Handling backfill operations (pause, unpause, cancel) programmatically.
When Not to Use
- When interacting with Airflow internal database directly (use standard SQL queries instead).
- When modifying DAG source files locally (use
pythonor standard editing skills instead). - When using Airflow UI-only endpoints (endpoints under
/uiare subject to breaking changes).
Core Process
- Authentication: Determine the authentication method configured for the Airflow API (e.g., Basic Auth, OAuth2). For Basic Auth, use
--user <username>:<password>. - Endpoint Identification: Identify the appropriate
/api/v2/endpoint from the API mindmap or documentation. - Payload Construction: For
POST,PUT, orPATCHrequests, construct the correct JSON payload (e.g.,{"is_paused": true}). - Execution: Use
curlor a scripting tool (like Python'srequests) to execute the API call. Include appropriate headers (e.g.,-H "Content-Type: application/json"). - Response Validation: Ensure the response is HTTP
200,201, or204. If401or403, verify credentials. If422, verify payload format.
Best Practices
- Stable Endpoints: Always use the
/api/v2/(or/api/v1/depending on the Airflow environment version) stable endpoints, avoiding/uiendpoints. - Filtering and Pagination: Use
limit,offset, andorder_byquery parameters when querying large collections like Task Instances or DAG Runs. - Dry Runs: Use the
dry_runendpoints (e.g.,/api/v2/backfills/dry_run,/api/v2/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/dry_run) to validate operations before committing them.
Mindmap of API Paths
The following mindmap outlines the structure of the Apache Airflow Stable REST API endpoints:
mindmap
root(("Airflow API"))
/api
/v2
/assets
/aliases
/{asset_alias_id}
/events
/{asset_id}
/materialize
/queuedEvents
/auth
/login
/logout
/backfills
/dry_run
/{backfill_id}
/cancel
/pause
/unpause
/config
/section
/{section}
/option
/{option}
/connections
/defaults
/test
/{connection_id}
/dagSources
/{dag_id}
/dagStats
/dagTags
/dagWarnings
/dags
/{dag_id}
/assets
/queuedEvents
/{asset_id}
/queuedEvents
/clearTaskInstances
/dagRuns
/list
/{dag_run_id}
/clear
/hitlDetails
/taskInstances
/list
/{task_id}
/dependencies
/dry_run
/externalLogUrl
/{try_number}
/links
/listMapped
/logs
/{try_number}
/tries
/{task_try_number}
/xcomEntries
/{xcom_key}
/{map_index}
/dependencies
/dry_run
/hitlDetails
/tries
/{try_number}
/tries
/{task_try_number}
/upstreamAssetEvents
/wait
/dagVersions
/{version_number}
/details
/favorite
/tasks
/{task_id}
/unfavorite
/eventLogs
/{event_log_id}
/importErrors
/{import_error_id}
/jobs
/monitor
/health
/parseDagFile
/{file_token}
/plugins
/importErrors
/pools
/{pool_name}
/providers
/variables
/{variable_key}
/versionCommon Pitfalls
- Authentication Missing: Forgetting to pass the bearer token or basic auth credentials, resulting in a
401 Unauthorizedresponse. - Incorrect Content Type: Omitting
-H "Content-Type: application/json"when executingPOSTorPATCHrequests, which Airflow will reject. - URL Encoding Paths: Forgetting to URL-encode variables, connection IDs, or DAG IDs that contain special characters.
- Timezone Mismatches: Airflow expects timestamps in strict ISO-8601 format (e.g.,
2026-05-20T12:00:00Z). Passing timezone-naive strings can cause validation errors.
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