Install
$ agentstack add skill-cogni-ai-ou-cogni-ai-agent-skills-apache-airflow-api ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
apache-airflow-api
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 :. - 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}
/version
Common 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.
References
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Cogni-AI-OU
- Source: Cogni-AI-OU/cogni-ai-agent-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.