airflow-adapter
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across…
Builds human-in-the-loop (HITL) Airflow workflows - approval gates, form input, and human-driven branching. Use when a DAG needs a human in the loop - an approval or reject step, sign-off before a task runs, a decision or approval UI, branching on a human choice, or collecting
$ npx -y skills add astronomer/agents --skill airflow-hitl --agent claude-codeHow it fires
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/airflow-hitlContext preview
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Builds human-in-the-loop (HITL) Airflow workflows - approval gates, form input, and human-driven branching. Use when a DAG needs a human in the loop - an approval or reject step, sign-off before a task runs, a decision or approval UI, branching on a human choice, or collecting
name: airflow-hitl description: Builds human-in-the-loop (HITL) Airflow workflows - approval gates, form input, and human-driven branching. Use when a DAG needs a human in the loop - an approval or reject step, sign-off before a task runs, a decision or approval UI, branching on a human choice, or collecting form input mid-run; also on mentions of ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, or HITLTrigger. Requires Airflow 3.1+. Not for AI/LLM task calls (see migrating-ai-sdk-to-common-ai).
Pause a DAG until a human responds via the Airflow UI or REST API. HITL operators are deferrable — they release their worker slot while waiting.
> **Requires Airflow 3.1+** (`af config version`). > > **UI location**: Browse → Required Actions. Respond from the task instance page's Required Actions tab. > > **Cross-references**: `migrating-ai-sdk-to-common-ai` for AI/LLM task decorators; `airflow` for registry and API discovery commands used below.
---
| Capability | Class (verify in Step 2) | |---|---| | Approve or reject; downstream skips on reject | `ApprovalOperator` | | Present N options and return which were chosen | `HITLOperator` | | Branch to one or more downstream tasks based on a choice | `HITLBranchOperator` | | Collect a form (no approve/select step) | `HITLEntryOperator` | | Use the HITL trigger directly (advanced / custom operators) | `HITLTrigger` |
This is the only place class names are hardcoded. The provider adds, renames, and removes params across releases — do not copy parameter lists from memory. Fetch the current signature before writing code.
---
Before writing HITL code, run these to see the live roster and constructor params (see the `airflow` skill for the full `af registry` reference):
# Every HITL-related module in the standard provider
af registry modules standard \
| jq '.modules[] | select(.import_path | test("\\.hitl\\.")) | {name, type, import_path, short_description, docs_url}'
# Constructor signatures: name, type, default, required, description
af registry parameters standard \
| jq '.classes | to_entries[] | select(.key | test("\\.hitl\\.")) | {fqn: .key, parameters: .value.parameters}'
# Pin to the exact installed provider version
af config providers \
| jq '.providers[] | select(.package_name == "apache-airflow-providers-standard") | .version'
# then: af registry parameters standard --version <VERSION>If the registry shows a param that this skill does not mention, prefer the registry. If the registry shows a class that is not in Step 1, treat it as additive — the decision table above may be stale.
---
Starting point for any HITL task. Adapt by swapping the class name and params per Step 2.
from airflow.providers.standard.operators.hitl import ApprovalOperator
from airflow.sdk import dag, task, chain, Param
from pendulum import datetime
@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def approval_example():
@task
def prepare():
return "Review quarterly report"
approval = ApprovalOperator(
task_id="approve_report",
subject="Report Approval",
body="{{ ti.xcom_pull(task_ids='prepare') }}",
defaults="Approve", # Auto-selected on timeout
params={"comments": Param("", type="string")},
)
@task
def after_approval(result):
print(f"Decision: {result['chosen_options']}")
chain(prepare(), approval)
after_approval(approval.output)
approval_example()For the other classes in Step 1, the shape is the same (`task_id`, `subject`, plus class-specific params). Verify each constructor through Step 2 — for example, `HITLBranchOperator` requires every option either to match a downstream task id directly or to be resolved via a mapping param surfaced in the registry.
---
`body` supports Markdown and is Jinja-templatable. Render XCom context directly:
body = """**Total Budget:** {{ ti.xcom_pull(task_ids='get_budget') }}
| Category | Amount |
|----------|--------|
| Marketing | $1M |
"""All HITL operators accept the standard Airflow callback kwargs (`on_success_callback`, `on_failure_callback`, etc.).
HITL operators accept a `notifiers` list. Inside a notifier's `notify(context)` method, build a link to the pending task with `HITLOperator.generate_link_to_ui_from_context(context, base_url=...)`.
The parameter name and accepted identifier format depend on the active auth manager. Do **not** hardcode — check which one is active and which kwarg the current provider exposes:
af config show | jq '.auth_manager // .core.auth_manager'
Then look up the current kwarg in Step 2 (at the time of writing it is `assigned_users`, accepting identifiers in whatever format the active auth manager uses — Astro uses the Astro user ID, FabAuthManager uses email, SimpleAuthManager uses username).
---
For Slack bots, custom apps, or scripts. Discover the live endpoint rather than hardcoding a path:
af api ls --filter hitl # live endpoint list
af api spec \
| jq '.paths | to_entries[] | select(.key | test("hitl"))' # request/response schemasThe PATCH-to-respond pattern is stable; the exact path is discovered. Typical shape:
import os, requests
HOST = os.environ["AIRFLOW_HOST"]
TOKEN = os.environ["AIRFLOW_API_TOKEN"]
HEADERS = {"Authorization": f"Bearer {TOKEN}"}
# List pending — use the path from `af api ls --filter hAI agent tooling for data engineering workflows. Includes an MCP server for Airflow, a CLI tool (af) for interacting with Airflow from your terminal, and skills that extend AI coding agents with specialized capabilities for working with Airflow and data
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