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debugging-dags

Comprehensive DAG failure diagnosis and root-cause analysis with structured investigation and prevention recommendations. Use when deep failure investigation is needed, a DAG fails to import/parse or 'airflow dags list' errors on a file; a task or run is failing and must be diagnosed and fixed; requests like 'why did X fail', 'my dag keeps failing — find and fix it', or fixing a broken DAG so it loads cleanly. For simple 'why did it fail / show logs', the airflow skill handles it directly.

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, well-sequenced diagnostic workflow with executable commands at every step and genuinely non-obvious domain knowledge (dependency drift detection via image diffs and index lookups). The main gaps are mild verbosity in the Prevention section, no validation checkpoint around the destructive clear/delete commands, and specialized package-drift content inlined rather than referenced.

Suggestions

Add a confirm-first checkpoint before the destructive Quick Commands (e.g., 'Before `af runs delete` or `af runs clear`, confirm with the user and check downstream dependencies'), which would also close the workflow_clarity validation gap.

Tighten the Prevention section's question list into a single concrete line (e.g., 'Prevention: data quality checks, error handling, alerting, or pinned dependencies as appropriate') to reduce filler tokens.

Consider moving the detailed 'Package version changes' subsection (image diff, venv-style operators, index lookup) into a reference file, keeping a one-line trigger in the main checklist.

DimensionReasoningScore

Conciseness

Mostly efficient operational prose with domain-specific knowledge Claude doesn't have (image diff for dependency drift, index lookup), but the Prevention section's question list ('Add data quality checks? Add better error handling? Add alerting…? Update documentation?') and a few explanatory asides could be tightened.

4 / 5

Actionability

Fully executable throughout: concrete CLI commands for every step (`af tasks logs <dag_id> <dag_run_id> <task_id>`), copy-paste docker `pip freeze` diffs, a ready-to-use PyPI curl/jq release query, and ready-to-use remediation commands covering the common cases.

5 / 5

Workflow Clarity

Clear four-step sequence (identify failure → get error details → check context → structured output) with a failure-type taxonomy for branching, but minor validation gap: the destructive `af runs clear` / `af runs delete` quick commands are offered without confirm-first or impact-check guidance.

4 / 5

Progressive Disclosure

Well-sectioned single-file skill with a working internal anchor ('[Package version changes](#package-version-changes)') and no bundle, but the ~30-line package-drift deep-dive is specialized content inlined in the main file rather than split into a reference.

4 / 5

Total

17

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it states concrete capabilities, provides an explicit 'Use when' clause with natural user phrasings and command-level triggers, and explicitly disambiguates from the simpler airflow skill. Only weakness is that the 'what' is phrased at capability level rather than concrete operational terms. (Minor typo: double space in 'root-cause analysis with'.)

DimensionReasoningScore

Specificity

Names the domain (DAG failures) and several actions ('failure diagnosis and root-cause analysis with structured investigation and prevention recommendations'), but the actions are capability-level rather than concrete operations, leaving minor gaps in coverage.

4 / 5

Completeness

Explicitly answers both 'what' (comprehensive DAG failure diagnosis and root-cause analysis with structured investigation and prevention recommendations) and 'when' (a clear 'Use when…' clause with concrete trigger conditions).

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including quoted user phrasings ('why did X fail', 'my dag keeps failing — find and fix it'), command-level triggers ('airflow dags list' errors, a DAG fails to import/parse), and the variation 'a task or run is failing'.

5 / 5

Distinctiveness Conflict Risk

Clear niche (deep DAG failure investigation) with explicit de-confliction guidance: 'For simple 'why did it fail / show logs', the airflow skill handles it directly' — minimal risk of triggering the wrong skill.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

astronomer/agents

Path

skills/debugging-dags/SKILL.md

Reviewed

Table of Contents

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