/testing-dags

name: testing-dags
description: Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.

DAG Testing Skill

Use `af` commands to test, debug, and fix DAGs in iterative cycles.

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`.

---

Quick Validation with Astro CLI

If the user has the Astro CLI available, these commands provide fast feedback without needing a running Airflow instance:

# Parse DAGs to catch import errors, syntax issues, and DAG-level problems
astro dev parse

# Run pytest against DAGs (runs tests in tests/ directory)
astro dev pytest

Use these for quick validation during development. For full end-to-end testing against a live Airflow instance, continue to the trigger-and-wait workflow below.

---

FIRST ACTION: Just Trigger the DAG

When the user asks to test a DAG, your **FIRST AND ONLY action** should be:

af runs trigger-wait <dag_id>

**DO NOT:**

• Call `af dags list` first
• Call `af dags get` first
• Call `af dags errors` first
• Use `grep` or `ls` or any other bash command
• Do any "pre-flight checks"

**Just trigger the DAG.** If it fails, THEN debug.

---

Testing Workflow Overview

┌─────────────────────────────────────┐
│ 1. TRIGGER AND WAIT                 │
│    Run DAG, wait for completion     │
└─────────────────────────────────────┘
                 ↓
        ┌───────┴───────┐
        ↓               ↓
   ┌─────────┐    ┌──────────┐
   │ SUCCESS │    │ FAILED   │
   │ Done!   │    │ Debug... │
   └─────────┘    └──────────┘
                       ↓
        ┌─────────────────────────────────────┐
        │ 2. DEBUG (only if failed)           │
        │    Get logs, identify root cause    │
        └─────────────────────────────────────┘
                       ↓
        ┌─────────────────────────────────────┐
        │ 3. FIX AND RETEST                   │
        │    Apply fix, restart from step 1   │
        └─────────────────────────────────────┘

**Philosophy: Try first, debug on failure.** Don't waste time on pre-flight checks — just run the DAG and diagnose if something goes wrong.

---

Phase 1: Trigger and Wait

Use `af runs trigger-wait` to test the DAG:

Primary Method: Trigger and Wait

af runs trigger-wait <dag_id> --timeout 300

**Example:**

af runs trigger-wait my_dag --timeout 300

**Why this is the preferred method:**

• Single command handles trigger + monitoring
• Returns immediately when DAG completes (success or failure)
• Includes failed task details if run fails
• No manual polling required

Response Interpretation

**Success:**

{
  "dag_run": {
    "dag_id": "my_dag",
    "dag_run_id": "manual__2025-01-14T...",
    "state": "success",
    "start_date": "...",
    "end_date": "..."
  },
  "timed_out": false,
  "elapsed_seconds": 45.2
}

**Failure:**

{
  "dag_run": {
    "state": "failed"
  },
  "timed_out": false,
  "elapsed_seconds": 30.1,
  "failed_tasks": [
    {
      "task_id": "extract_data",
      "state": "failed",
      "try_number": 2
    }
  ]
}

**Timeout:**

{
  "dag_id": "my_dag",
  "dag_run_id": "manual__...",
  "state": "running",
  "timed_out": true,
  "elapsed_seconds": 300.0,
  "message": "Timed out after 300 seconds. DAG run is still running."
}

Alternative: Trigger and Monitor Separately

Use this only when you need more control:

# Step 1: Trigger
af runs trigger my_dag
# Returns: {"dag_run_id": "manual__...", "state": "queued"}

# Step 2: Check status
af runs get my_dag manual__2025-01-14T...
# Returns current state

---

Handling Results

If Success

The DAG ran successfully. Summarize for the user:

• Total elapsed time
• Number of tasks completed
• Any notable outputs (if visible in logs)

**You're done!**

If Timed Out

The DAG is still running. Options: 1. Check current status: `af runs get <dag_id> <dag_run_id>` 2. Ask user if they want to continue waiting 3. Increase timeout and try again

If Failed

Move to Phase 2 (Debug) to identify the root cause.

---

Phase 2: Debug Failures (Only If Needed)

When a DAG run fails, use these commands to diagnose:

Get Comprehensive Diagnosis

af runs diagnose <dag_id> <dag_run_id>

Returns in one call:

• Run metadata (state, timing)
• All task instances with states
• Summary of failed tasks
• State counts (success, failed, skipped, etc.)

Get Task Logs

af tasks logs <dag_id> <dag_run_id> <task_id>

**Example:**

af tasks logs my_dag manual__2025-01-14T... extract_data

**For specific retry attempt:**

af tasks logs my_dag manual__2025-01-14T... extract_data --try 2

**Look for:**

• Exception messages and stack traces
• Connection errors (database, API, S3)
• Permission errors
• Timeout errors
• Missing dependencies

Check Upstream Tasks

If a task shows `upstream_failed`, the root cause is in an upstream task. Use `af runs diagnose` to find which task actually failed.

Check Import Errors (If DAG Didn't Run)

If the trigger failed because the DAG doesn't exist:

af dags errors

This reveals syntax errors or missing dependencies that prevented the DAG from loading.

---

Phase 3: Fix and Retest

Once you identify the issue:

Common Fixes

| Issue | Fix | |-------|-----| | Missing import | Add to DAG file | | Missing package | Add to `requirements.txt` | | Connection error | Check `af config connections`, verify credentials | | Variable missing | Check `af config variables`, create if needed | | Timeout | Increase task