airflow

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.

Quality

89%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

78%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-organized operations reference with concrete commands, clear intent routing, and mostly well-sequenced workflows. Its main defects are a dangling api-reference.md link (the referenced file is missing from the bundle), missing validation guidance around destructive commands, and some cross-section command redundancy.

Suggestions

Ship the referenced api-reference.md in the bundle (or remove the link): the body points to it for "all options, common endpoints (XCom, event-logs, backfills), and examples" but no such file exists, breaking the only progressive-disclosure path.

Add validation/caution steps for destructive and production-facing operations: `af runs delete` ("permanently delete"), `astro deploy`, and `af api ... -X DELETE` currently appear with no verify-first or confirmation guidance, unlike the well-handled `instance discover --dry-run` case.

Trim redundancy between the Quick Reference table and the User Intent Patterns section (e.g. `af dags list`, `af runs trigger`, `af config pools` appear in both) by keeping one as the canonical command lookup.

Dimension	Reasoning	Score
Conciseness	Command-dense tables and code blocks with no padding or explanations of concepts Claude already knows, but commands repeat across the Quick Reference table, User Intent Patterns, and Common Workflows sections (e.g. `af dags list` appears in all three), which could be consolidated.	4 / 5
Actionability	Fully executable, copy-paste-ready guidance throughout: concrete dag_ids and full run_ids, working jq pipelines (e.g. `af registry parameters standard \| jq '.classes[...]'.parameters'`), and exact flag semantics (`-F` auto-converts types, `-f` keeps strings).	5 / 5
Workflow Clarity	Numbered diagnostic sequences (Investigate a Failed Run steps 1-4), a dedicated validate-before-deploy workflow, and a consent checkpoint for `instance discover` ("Always run with --dry-run first and ask for user consent"), but destructive operations like `af runs delete` and `astro deploy` lack warnings or verification steps, capping it below 5.	4 / 5
Progressive Disclosure	The single bundle reference ("Full reference: See [api-reference.md](api-reference.md)") is well signaled and one level deep, but the file does not exist in the bundle (no references/ directory), so navigation is broken; the ~35-row inline command table is also content that belongs in a reference file.	3 / 5
	Total	16 / 20 Passed

Description

100%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.

An exemplary description: concrete third-person action list, explicit "Use when" triggers with natural example phrases, comprehensive keyword coverage, and explicit boundary guidance that disambiguates it from sibling skills. Every clause carries signal despite the length.

Dimension	Reasoning	Score
Specificity	Lists many concrete, comprehensive actions ("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") in third-person voice with no coverage gaps.	5 / 5
Completeness	Explicitly answers both "what" (the enumerated CLI capabilities) and "when" ("Use when working with anything related to Airflow - a DAG, a DAG run, a task log, an import or parse error, a broken DAG"), with concrete example trigger phrases.	5 / 5
Trigger Term Quality	Includes explicit natural phrases users would say ("trigger a pipeline", "retry a run", "list connections", "check Airflow health", "why did my DAG fail") plus domain nouns like DAG, DAG run, task log, and broken DAG.	5 / 5
Distinctiveness Conflict Risk	Clear niche (Airflow operations via the af CLI) with explicit negative boundaries ("Not for warehouse/SQL analytics... use analyzing-data; for deep root-cause reports use debugging-dags or airflow-investigation") and an explicit routing statement that resolves sibling-skill overlap.	5 / 5
	Total	20 / 20 Passed

Validation

87%

Warnings & errors only

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

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 1 missing	Warning
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	14 / 16 Passed

Repository: astronomer/agents
Path: skills/airflow/SKILL.md
Commit: 1ec1a1f

Reviewed: 1 day ago

Table of Contents

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