Airflow Hitl logo

Airflow Hitl

Organization
astronomer
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 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).

Overview

Publisherastronomer
Repositoryagents
Skill nameairflow-hitl
Stars
455
Forks
71
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by astronomer on GitHub. Read the source before you install it.

Installation

Install the Airflow Hitl AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/astronomer/agents/tree/main/skills/airflow-hitl
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/astronomer/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/airflow-hitl .claude/skills/airflow-hitl
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Airflow Hitl in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Airflow Hitl on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Airflow Hitl is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

CapabilityClass (verify in Step 2)
Approve or reject; downstream skips on rejectApprovalOperator
Present N options and return which were chosenHITLOperator
Branch to one or more downstream tasks based on a choiceHITLBranchOperator
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):

bash
# 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.

python
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:

python
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:

bash
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:

bash
af api ls --filter hitl           # live endpoint list
af api spec \
  | jq '.paths | to_entries[] | select(.key | test("hitl"))'   # request/response schemas

The PATCH-to-respond pattern is stable; the exact path is discovered. Typical shape:

python
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 hitl`
requests.get(f"{HOST}/<path>", headers=HEADERS, params={"state": "pending"})

# Respond — same discovered path family, PATCH
requests.patch(
    f"{HOST}/<path>/{dag_id}/{run_id}/{task_id}",
    headers=HEADERS,
    json={"chosen_options": ["Approve"], "params_input": {"comments": "ok"}},
)

Step 6 — Safety checks

  • Airflow version ≥ 3.1 (af config version).
  • Constructor kwargs match the current registry output from Step 2 — no respondents-vs-assigned_users style drift.
  • For branching: every option resolves to a downstream task id (directly or via the mapping kwarg from Step 2).
  • Every value in defaults is also in options.
  • execution_timeout set; defaults configured if timeout should succeed rather than fail.
  • API token configured if external responders are part of the flow.

References

The upstream docs URL is surfaced per-module by the registry — do not hardcode:

bash
af registry modules standard \
  | jq '.modules[] | select(.import_path | test("\\.hitl\\.")) | {name, docs_url}'

Related skills

  • airflow — af registry, af api, af config command reference.
  • migrating-ai-sdk-to-common-ai — AI/LLM task decorators and GenAI patterns (common-ai provider).
  • authoring-dags — general DAG writing best practices.
  • testing-dags — iterative test → debug → fix cycles.

Frequently asked questions

What does the Airflow Hitl AI skill do?

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).

Why use Airflow Hitl on TypingMind?

Because you install it once and use it with any model. Airflow Hitl is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Airflow Hitl in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/astronomer/agents/tree/main/skills/airflow-hitl. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Airflow Hitl?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Airflow Hitl?

As many as you like. As long as a model supports skills, you can use Airflow Hitl with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Airflow Hitl AI skill free?

Yes. It is published on GitHub by astronomer under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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