airflow
This skill provides command-line operations for Apache Airflow, enabling users to query DAGs, trigger runs, read task logs, diagnose failures, and manage Airflow instances through the `af` CLI and Astro CLI. Use it when users need to perform Airflow administrative tasks like listing DAGs, checking connections, debugging import errors, deploying projects, or troubleshooting workflow issues, while routing to specialized sub-skills for writing DAGs, configuring local environments, or detailed deployments.
git clone --depth 1 https://github.com/astronomer/agents /tmp/airflow && cp -r /tmp/airflow/skills/airflow ~/.claude/skills/airflowSKILL.md
# Airflow Operations
Use `af` commands to query, manage, and troubleshoot Airflow workflows.
## Astro CLI
The [Astro CLI](https://www.astronomer.io/docs/astro/cli/overview) is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:
```bash
# Initialize a new project
astro dev init
# Start local Airflow (webserver at http://localhost:8080)
astro dev start
# Parse DAGs to catch errors quickly (no need to start Airflow)
astro dev parse
# Run pytest against your DAGs
astro dev pytest
# Deploy to production
astro deploy # Full deploy (image + DAGs)
astro deploy --dags # DAG-only deploy (fast, no image build)
```
For more details:
- **New project?** See the **setting-up-astro-project** skill
- **Local environment?** See the **managing-astro-local-env** skill
- **Deploying?** See the **deploying-airflow** skill
---
## Running the CLI
These commands assume `af` is on PATH. Run via `astro otto` to get it automatically, or install standalone with `uv tool install astro-airflow-mcp`.
## Instance Configuration
Manage multiple Airflow instances with persistent configuration:
```bash
# Add a new instance
af instance add prod --url https://airflow.example.com --token "$API_TOKEN"
af instance add staging --url https://staging.example.com --username admin --password admin
# List and switch instances
af instance list # Shows all instances in a table
af instance use prod # Switch to prod instance
af instance current # Show current instance
af instance delete old-instance
# Auto-discover instances (use --dry-run to preview first)
af instance discover --dry-run # Preview all discoverable instances
af instance discover # Discover from all backends (astro, local)
af instance discover astro # Discover Astro deployments only
af instance discover astro --all-workspaces # Include all accessible workspaces
af instance discover local # Scan common local Airflow ports
af instance discover local --scan # Deep scan all ports 1024-65535
# IMPORTANT: Always run with --dry-run first and ask for user consent before
# running discover without it. The non-dry-run mode creates API tokens in
# Astro Cloud, which is a sensitive action that requires explicit approval.
# Show where an instance came from (file path + scope)
af instance show prod
# Override instance for a single command via env vars
AIRFLOW_API_URL=https://staging.example.com AIRFLOW_AUTH_TOKEN=$STG af dags list
# Or switch persistently
af instance use staging
```
Config layout (mirrors `git config` system/global/local):
| Scope | File | Committed? |
|---|---|---|
| Global | `~/.astro/config.yaml` | n/a (per-user) |
| Project shared | `<root>/.astro/config.yaml` | yes |
| Project local | `<root>/.astro/config.local.yaml` | no (gitignored) |
`<root>` is found by walking up from cwd looking for `.astro/`. Default write routing inside a project: `add`/`discover` → project-shared, `use` → project-local. Override with `--global` / `--project` / `--local`. Set `AF_CONFIG=<path>` to bypass layering and use a single file.
Migrate from the legacy `~/.af/config.yaml` with `af migrate` (idempotent; renames the old file to `.bak`).
Tokens in config can reference environment variables using `${VAR}` syntax:
```yaml
instances:
- name: prod
url: https://airflow.example.com
auth:
token: ${AIRFLOW_API_TOKEN}
```
Or use environment variables directly (no config file needed):
```bash
export AIRFLOW_API_URL=http://localhost:8080
export AIRFLOW_AUTH_TOKEN=your-token-here
# Or username/password:
export AIRFLOW_USERNAME=admin
export AIRFLOW_PASSWORD=admin
```
Or CLI flags: `af --airflow-url http://localhost:8080 --token "$TOKEN" <command>`
## Quick Reference
| Command | Description |
|---------|-------------|
| `af health` | System health check |
| `af dags list` | List all DAGs |
| `af dags get <dag_id>` | Get DAG details |
| `af dags explore <dag_id>` | Full DAG investigation |
| `af dags source <dag_id>` | Get DAG source code |
| `af dags pause <dag_id>` | Pause DAG scheduling |
| `af dags unpause <dag_id>` | Resume DAG scheduling |
| `af dags errors` | List import errors |
| `af dags warnings` | List DAG warnings |
| `af dags stats` | DAG run statistics |
| `af runs list` | List DAG runs |
| `af runs get <dag_id> <run_id>` | Get run details |
| `af runs trigger <dag_id>` | Trigger a DAG run |
| `af runs trigger-wait <dag_id>` | Trigger and wait for completion |
| `af runs delete <dag_id> <run_id>` | Permanently delete a DAG run |
| `af runs clear <dag_id> <run_id>` | Clear a run for re-execution |
| `af runs diagnose <dag_id> <run_id>` | Diagnose failed run |
| `af tasks list <dag_id>` | List tasks in DAG |
| `af tasks get <dag_id> <task_id>` | Get task definition |
| `af tasks instance <dag_id> <run_id> <task_id>` | Get task instance |
| `af tasks logs <dag_id> <run_id> <task_id>` | Get task logs |
| `af config version` | Airflow version |
| `af config show` | Full configuration |
| `af config connections` | List connections |
| `af config variables` | List variables |
| `af config variable <key>` | Get specific variable |
| `af config pools` | List pools |
| `af config pool <name>` | Get pool details |
| `af config plugins` | List plugins |
| `af config providers` | List providers |
| `af config assets` | List assets/datasets |
| `af api <endpoint>` | Direct REST API access |
| `af api ls` | List available API endpoints |
| `af api ls --filter X` | List endpoints matching pattern |
| `af registry providers` | List providers in the Airflow Registry |
| `af registry modules <provider>` | List operators/hooks/sensors/transfers in a provider |
| `af registry parameters <provider>` | Constructor signatures (name, type, default, required) for a provider's classes |
| `af registry connections <provider>` | Connection types a provider exposes |
## User Intent Patterns
### Getting Started
-Add a new method to both Airflow adapters
Add a new MCP tool to server.py
Verify code works with both Airflow 2.x and 3.x
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.
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
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.