authoring-dags

Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.

Quality

83%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

77%

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

This is a well-structured skill with strong workflow clarity and actionability — the phased approach with explicit validation steps and CLI commands makes it highly usable. The main weaknesses are moderate verbosity (duplicated CLI reference table, bulky ASCII diagram) and incomplete progressive disclosure since the referenced best-practices.md bundle file is missing, leaving Phase 3 (Implement) hollow.

Suggestions

Remove or condense the ASCII workflow diagram to a simple numbered list — the phases are already clearly documented in the sections below it.

Eliminate the CLI Quick Reference table since every command is already shown in context within its respective phase section, or keep only the table and remove inline duplicates.

Provide the referenced reference/best-practices.md as a bundle file, or inline the most critical patterns directly in Phase 3 so the skill isn't dependent on a missing file.

Dimension	Reasoning	Score
Conciseness	Generally efficient but has some redundancy — the ASCII workflow diagram is bulky for what it conveys, the CLI quick reference table duplicates commands already shown in phases, and testing guidance is repeated across multiple sections despite deferring to another skill. Some tightening possible.	2 / 3
Actionability	Provides specific, executable CLI commands for every phase (af dags errors, af dags get, af runs trigger-wait, etc.), concrete file patterns to glob, and clear tables mapping questions to commands. Guidance is copy-paste ready throughout.	3 / 3
Workflow Clarity	Clear 6-phase sequence with explicit validation checkpoints in Phase 4 (check errors → verify DAG exists → check warnings → explore structure), a feedback loop in Phase 6 (fix → re-validate → re-test), and user consent gates before testing. The workflow handles error recovery well.	3 / 3
Progressive Disclosure	References to testing-dags skill and reference/best-practices.md are well-signaled and one level deep. However, no bundle files were provided, so the referenced best-practices.md doesn't exist, and the related skills section lists skills (debugging-dags, deploying-airflow, migrating-airflow-2-to-3) without clear links. The main content is reasonably structured but the Phase 3 (Implement) section is thin, deferring entirely to a best-practices file that isn't bundled.	2 / 3
	Total	10 / 12 Passed

Description

89%

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

This is a well-structured skill description with a clear 'Use when' clause, good trigger terms, and explicit scope boundaries (distinguishing from the testing-dags skill). Its main weakness is that the 'what' portion is somewhat general—describing 'workflow and best practices' rather than listing specific concrete actions like defining operators, setting schedules, or configuring task dependencies.

Suggestions

Add more specific concrete actions to the capability description, e.g., 'Guides creation of Airflow DAGs including defining operators, setting schedules, configuring task dependencies, and structuring pipeline code.'

Dimension	Reasoning	Score
Specificity	Names the domain (Apache Airflow DAGs) and mentions some actions ('create a new DAG', 'write pipeline code', 'DAG patterns and conventions'), but doesn't list specific concrete actions like defining operators, setting schedules, configuring dependencies, or handling retries.	2 / 3
Completeness	Clearly answers both 'what' (workflow and best practices for writing Airflow DAGs) and 'when' (explicit 'Use when' clause covering creating DAGs, writing pipeline code, or asking about patterns). Also helpfully delineates scope by pointing to a separate testing skill.	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'Airflow', 'DAG', 'DAGs', 'pipeline code', 'DAG patterns', 'conventions', 'create a new DAG'. These cover the most common ways users would phrase requests about writing Airflow DAGs.	3 / 3
Distinctiveness Conflict Risk	Clearly scoped to Apache Airflow DAG authoring with explicit boundary drawn against the testing-dags skill. The combination of 'Airflow', 'DAG', and 'pipeline code' creates a distinct niche unlikely to conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: astronomer/agents
Commit: 789b454

Reviewed: 8 days ago

Table of Contents

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