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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.

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

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.

An operationally excellent CLI reference: every command is concrete and executable, workflows are sequenced with feedback loops, and safety-sensitive operations (instance discovery) carry explicit consent guidance. The weaknesses are structural: content duplicated between the Quick Reference and User Intent Patterns sections, and a well-signaled link to api-reference.md whose target is missing from the bundle.

Suggestions

Add the referenced api-reference.md to the bundle (or drop/replace the link) - as written, the only progressive-disclosure pointer in the skill leads to a file that does not exist, breaking navigation for the af api details it promises.

De-duplicate the command surface: either fold the User Intent Patterns natural-language mappings into the Quick Reference table or move the full intent catalog into a reference file, so each command appears once in SKILL.md.

Add a validation step for the destructive 'af runs delete' command (e.g., check the run's state/details with 'af runs get' and confirm with the user before deleting), matching the consent guidance already present for instance discovery.

DimensionReasoningScore

Conciseness

The body is dense and assumes competence (no 'what is Airflow' padding), but there is real duplication: the command set appears in both the Quick Reference table and again per-command in User Intent Patterns, and `astro dev parse` / `astro dev pytest` are shown in the Astro CLI section then repeated in 'Validate DAGs Before Deploying'. This is more than anchor 3's 'some unnecessary explanation' but only minor trims needed for anchor 5.

4 / 5

Actionability

Fully executable throughout: concrete commands with real arguments ("af runs diagnose my_dag manual__2024-01-15T10:00:00+00:00"), ready-to-run jq pipelines ("jq '.dag_runs[] | select(.state == \"failed\")'"), flag semantics ("-F key=value auto-converts types, -f key=value keeps as string"), and edge-case options (--try 2, --map-index 5). Copy-paste ready and covers the common cases.

5 / 5

Workflow Clarity

'Investigate a Failed Run' is a clean numbered sequence with a feedback loop (diagnose -> logs -> fix -> clear), 'Validate DAGs Before Deploying' gives two validation paths, and instance discovery has an explicit consent checkpoint ('Always run with --dry-run first and ask for user consent'). The gap keeping it below 5: the destructive "af runs delete" has no check-state-before-delete or confirmation guidance anywhere.

4 / 5

Progressive Disclosure

Sections are well-organized and the single external reference is clearly signaled ("**Full reference**: See [api-reference.md](api-reference.md) for all options..."), but that file does not exist in the bundle - the link dangles, so navigation fails at the point of use. The ~40-row command reference and the full User Intent Patterns catalog are also fully inline in a single 390-line file, which fits anchor 3 ('content that should be separate is inline') rather than anchor 4's 'minor organization gaps'.

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.

The description is an exemplar: it names the tool and concrete capabilities, provides explicit 'Use when' triggers with natural quoted phrasings, and disambiguates against sibling skills with negative boundaries. It is dense with specifics rather than padded, so its length reflects information, not fluff.

DimensionReasoningScore

Specificity

"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" enumerates roughly ten concrete, distinct actions with comprehensive coverage of the skill's domain, matching the top anchor rather than the 'minor gaps' anchor below it.

5 / 5

Completeness

Explicitly answers both questions: what ("Queries, manages, and troubleshoots Apache Airflow using the `af` CLI" plus the enumerated capabilities) and when ("Use when working with anything related to Airflow - a DAG, a DAG run, a task log, an import or parse error..." with concrete example trigger phrases). This is a direct match for the top anchor.

5 / 5

Trigger Term Quality

Includes natural user phrasings in quotes ("trigger a pipeline", "retry a run", "list connections", "check Airflow health", "why did my DAG fail") plus synonyms and error language ("a DAG run", "a task log", "an import or parse error", "a broken DAG", "migrating Airflow 2 to 3"). Coverage is comprehensive including synonyms; only trivially related terms are absent, so it fits the 5 anchor rather than 4.

5 / 5

Distinctiveness Conflict Risk

Clear niche (Airflow operations via the `af` CLI) with explicit negative boundaries routing elsewhere: "Not for warehouse/SQL analytics on Airflow metadata tables (use analyzing-data); for deep root-cause reports use debugging-dags or airflow-investigation". The broad "anything related to Airflow" trigger is intentional entrypoint design with sibling routing stated, so conflict risk is minimal rather than merely minor.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

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

Reviewed

Table of Contents

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