Airflow

What it does

When your agent uses it

• Working with anything related to Airflow - a DAG
• Any Airflow operation
• List connections
• Check Airflow health

Example prompts

• “trigger a pipeline”
• “retry a run”
• “list connections”
• “/airflow”

Requirements

• A Bash shell
• A credential in API_TOKEN
• A credential in AIRFLOW_AUTH_TOKEN

What it can do on your machine

Read from SKILL.md and the folder at commit 486ee63. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Shell), which the agent can run.

  Shell commands in SKILL.md call:

  • jq
  • uv
  • git

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • astronomer.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • AIRFLOW_AUTH_TOKEN
  • API_TOKEN
  • AIRFLOW_API_TOKEN
  • AIRFLOW_PASSWORD

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Airflow loads about 3.8k tokens when it runs. Until then it costs about 208 tokens; SKILL.md has 1,232 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~208

When it runs · the whole SKILL.md, loaded when a task matches

~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from astronomer/agents at commit 486ee63, republished under its Apache-2.0 licence (© astronomer). 1,232 words, ~3,817 tokens.

name

airflow

description

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 task logs, diagnosing failures, debugging import and parse errors, checking connections, variables and pools, exploring the REST API, and monitoring health (for example "trigger a pipeline", "retry a run", "list connections", "check Airflow health", "why did my DAG fail"). This is the entrypoint that routes to sibling skills for authoring, testing, deploying, and migrating Airflow 2 to 3. Not for warehouse/SQL analytics on Airflow metadata tables (use analyzing-data); for deep root-cause reports use debugging-dags or airflow-investigation.

# 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)

# 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

instances:
- name: prod
  url: https://airflow.example.com
  auth:
    token: ${AIRFLOW_API_TOKEN}

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

# Parse DAGs to catch import errors and syntax issues
astro dev parse

# Run unit tests
astro dev pytest

af dags errors     # Check for parse/import errors
af dags warnings   # Check for deprecation warnings

# List all providers and pick the one you need
af registry providers | jq '.providers[] | {id, name, version}'

# List every operator / hook / sensor in a provider (e.g. standard, amazon, google)
af registry modules standard \
  | jq '.modules[] | {name, type, import_path, docs_url}'

# Get the current constructor signature for a specific class
af registry parameters standard \
  | jq '.classes["airflow.providers.standard.operators.hitl.ApprovalOperator"].parameters'

# Filter modules by substring (useful when you know the concept but not the class)
af registry modules standard \
  | jq '.modules[] | select(.import_path | test("hitl"))'

# 1. List recent runs to find failure
af runs list --dag-id my_dag

# 2. Diagnose the specific run
af runs diagnose my_dag manual__2024-01-15T10:00:00+00:00

# 3. Get logs for failed task (from diagnose output)
af tasks logs my_dag manual__2024-01-15T10:00:00+00:00 extract_data

# 4. After fixing, clear the run to retry all tasks
af runs clear my_dag manual__2024-01-15T10:00:00+00:00

# 1. Overall system health
af health

# 2. Check for broken DAGs
af dags errors

# 3. Check pool utilization
af config pools

# Get comprehensive overview (metadata + tasks + source)
af dags explore my_dag

# Check if paused
af dags get my_dag

# Check for import errors
af dags errors

# Check recent runs
af runs list --dag-id my_dag

# Option 1: Trigger and wait (blocking)
af runs trigger-wait my_dag --timeout 1800

# Option 2: Trigger and check later
af runs trigger my_dag
af runs get my_dag <run_id>

af dags list
# {
#   "total_dags": 5,
#   "returned_count": 5,
#   "dags": [...]
# }

# Find failed runs
af runs list | jq '.dag_runs[] | select(.state == "failed")'

# Get DAG IDs only
af dags list | jq '.dags[].dag_id'

# Find paused DAGs
af dags list | jq '[.dags[] | select(.is_paused == true)]'

# Get logs for specific retry attempt
af tasks logs my_dag run_id task_id --try 2

# Get logs for mapped task index
af tasks logs my_dag run_id task_id --map-index 5

# Discover available endpoints
af api ls
af api ls --filter variable

# Basic usage
af api dags
af api dags -F limit=10 -F only_active=true
af api variables -X POST -F key=my_var -f value="my value"
af api variables/old_var -X DELETE

© astronomer, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/airflow of astronomer/agents.

• SKILL.md
• api-reference.md
• hooks/warm-uvx-cache.sh

Open the folder on GitHubat commit 486ee63

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in astronomer/agents, which our catalogue first saw on October 7, 2026.

• Kilo-Org/kilo-marketplaceskills/airflow