What is debugging-dags?

A structured guide for diagnosing failed Apache Airflow workflows, called DAGs. It helps trace a failure from the workflow or run to the task logs and underlying error.

Use it to investigate broken or failed DAGs, inspect task logs, identify data or code problems, and recommend fixes and ways to prevent repeat failures.

It replaces guesswork with a sequence for finding recent failures, import errors, the real exception, and the likely category of the problem.

debugging-dags — Skill for Claude Code by astronomer/agents

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add astronomer/agents --skill debugging-dags

Clone the repo

git clone --depth 1 https://github.com/astronomer/agents

Made for: Claude Code.

Or install astronomer-data, the plugin that ships this one along with the rest of its 35 skills, 3 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/astronomer/agents/debugging-dags/github.svg)](https://agentmods.dev/skills/astronomer/agents/debugging-dags)

Your own site

<a href="https://agentmods.dev/skills/astronomer/agents/debugging-dags"><img src="https://agentmods.dev/badge/skills/astronomer/agents/debugging-dags/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/astronomer/agents/debugging-dags"><img src="https://agentmods.dev/badge/skills/astronomer/agents/debugging-dags.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,802 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 15 Sept 2026
Snyk warn 15 Sept 2026
NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 3 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium MCP Rug Pull · line 59
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 60
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 63
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00111	$0.01802
Opus 5.5	$0.00044	$0.00721
Sonnet 5.5	$0.00022	$0.00360
Haiku 4.5	$0.00011	$0.00180

Measured 1mo ago against content hash 72e6f2caf8cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

debugging-dags scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s https://pypi.org/pypi/<pkg>/json | jq '.releases | to_entries | map({version: .key, uploaded: .value[0].upload_time}) | sort_by(.uploaded) | reverse | .[:5]'

skills/debugging-dags/SKILL.md · 129 lines

How it starts

The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DAG Diagnosis

You are a data engineer debugging a failed Airflow DAG. Follow this systematic approach to identify the root cause and provide actionable remediation.

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.

Step 1: Identify the Failure

If a specific DAG was mentioned:

Run af runs diagnose <dag_id> <dag_run_id> (if run_id is provided)
If no run_id specified, run af dags stats to find recent failures

If no DAG was specified:

Run af health to find recent failures across all DAGs
Check for import errors with af dags errors
Show DAGs with recent failures
Ask which DAG to investigate further

Step 2: Get the Error Details

Once you have identified a failed task:

Get task logs using af tasks logs <dag_id> <dag_run_id> <task_id>
Look for the actual exception - scroll past the Airflow boilerplate to find the real error
Categorize the failure type:
- Data issue: Missing data, schema change, null values, constraint violation
- Code issue: Bug, syntax error, import failure, type error
- Infrastructure issue: Connection timeout, resource exhaustion, permission denied
- Dependency issue: Upstream failure, external API down, rate limiting

Step 3: Check Context

Gather additional context to understand WHY this happened:

Recent changes: Was there a code deploy? Check git history if available
Package version changes: Was a package upgraded — in the image, in a venv-style operator, or at the index? See Package version changes below.
Data volume: Did data volume spike? Run a quick count on source tables
Upstream health: Did upstream tasks succeed but produce unexpected data?
Historical pattern: Is this a recurring failure? Check if same task failed before
Timing: Did this fail at an unusual time? (resource contention, maintenance windows)

Read the full file on GitHub · 129 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 129 lines · 111 tokens per session scan A 72e6f2caf8cf

Subscribe to this mod's changes

debugging-dags is a skill published in the GitHub repository astronomer/agents (453 stars, last pushed 2d ago), licensed Apache-2.0. It adds 111 tokens to every session and 1,802 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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debugging-dags