/airflow-hitl
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
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/airflow-hitl
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
airflow-hitl.SKILL.mdname: 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).
Airflow Human-in-the-Loop Operators
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.
---
Step 1 — Pick the capability you need
| 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.
---
Step 2 — Discover the current signatures from the Airflow Registry
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.
---
Step 3 — Canonical example (approval gate)
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.
---
Step 4 — Behavior contracts (stable across versions)
Timeout
- With `defaults` set: task succeeds on timeout, default option(s) selected.
- Without `defaults`: task fails on timeout.
Markdown + Jinja in `body`
`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 |
"""Callbacks
All HITL operators accept the standard Airflow callback kwargs (`on_success_callback`, `on_failure_callback`, etc.).
Notifiers
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=...)`.
Restricting who can respond
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).
---
Step 5 — Responding from external integrations
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 hRead more
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).
Airflow Human-in-the-Loop Operators
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.
---
Step 1 — Pick the capability you need
| 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.
---
Step 2 — Discover the current signatures from the Airflow Registry
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.
---
Step 3 — Canonical example (approval gate)
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.
---
Step 4 — Behavior contracts (stable across versions)
Timeout
- With `defaults` set: task succeeds on timeout, default option(s) selected.
- Without `defaults`: task fails on timeout.
Markdown + Jinja in `body`
`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 |
"""Callbacks
All HITL operators accept the standard Airflow callback kwargs (`on_success_callback`, `on_failure_callback`, etc.).
Notifiers
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=...)`.
Restricting who can respond
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).
---
Step 5 — Responding from external integrations
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
Other skills on data.
- /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 Airflow 2.x and 3.x.
Open skill - /airflow-plugins
Builds Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use when building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface - creating an
Open skill - /airflow-state-store
Persists task and asset state across retries and DAG runs using Airflow 3.3's AIP-103 key/value stores (`task_state_store`, `asset_state_store`) and the crash-safe `ResumableJobMixin`. Use when the user asks about task state store, checkpointing in tasks, persisting state across
Open skill - /airflow
Queries, manages, and troubleshoots Apache Airflow using the `af` CLI. Use when working with anything related to Airflow - a DAG, a DAG run, a task log, an import or parse error, a broken DAG, or any Airflow operation. Covers listing and triggering DAGs, retrying runs, reading
Open skill - /analyzing-data
Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show
Open skill - /annotating-task-lineage
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
Open skill

