blueprint

Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.

Quality

88%

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 strong, highly actionable skill with excellent executable examples and clear workflow sequencing. Its main weakness is length — at ~300 lines with no bundle files for progressive disclosure, it consumes significant context window space. Some sections could be trimmed or split into referenced files to improve token efficiency while maintaining the excellent actionability.

Suggestions

Split detailed sections (Runtime Parameter Overrides, Post-Build Callbacks, Versioning, Schema Generation) into separate bundle files and reference them from the main SKILL.md to reduce token footprint.

Remove the 'Before Starting' confirmation step — Claude can check versions reactively rather than always asking upfront, and the DAG Factory recommendation is unnecessary context.

Dimension	Reasoning	Score
Conciseness	The skill is generally well-structured but includes some unnecessary verbosity. The routing table at the top is helpful but adds length. Some sections like 'Before Starting' with version confirmation and the 'use case' disclaimer explain things Claude could infer. The troubleshooting section is efficient, but overall the document is quite long (~300 lines) and could be tightened in places.	2 / 3
Actionability	Excellent actionability throughout — every section provides concrete, executable code examples (Python classes, YAML configs, bash commands). The blueprint structure, YAML composition, CLI commands, and troubleshooting fixes are all copy-paste ready with specific syntax and patterns.	3 / 3
Workflow Clarity	The skill provides clear sequenced workflows: Project Setup has numbered steps (install → create loader → verify), the validation workflow has explicit commands with expected output, and the verification checklist at the end serves as a final validation checkpoint. The routing table at the top clearly directs to the right section based on user needs.	3 / 3
Progressive Disclosure	The content is well-organized with clear section headers and a routing table, but it's a monolithic document with no references to external files in a bundle. Several sections (like Runtime Parameter Overrides, Post-Build Callbacks, Versioning) could be split into separate reference files to keep the main SKILL.md leaner. For a skill of this complexity, the single-file approach makes it quite long.	2 / 3
	Total	10 / 12 Passed

Description

100%

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 strong skill description that clearly articulates specific capabilities (reusable task group templates, Pydantic validation, YAML DAG composition) and provides explicit trigger guidance via a 'Use when...' clause with multiple scenarios. The description is concise, uses third-person voice, and occupies a distinct niche that minimizes conflict risk with other skills.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Define reusable Airflow task group templates', 'Pydantic validation', 'compose DAGs from YAML', 'validating configurations', 'no-code DAG authoring'. These are concrete, actionable capabilities.	3 / 3
Completeness	Clearly answers both 'what' (define reusable task group templates with Pydantic validation, compose DAGs from YAML) and 'when' with an explicit 'Use when...' clause listing four trigger scenarios: creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring.	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'Airflow', 'task group', 'templates', 'Pydantic', 'DAGs', 'YAML', 'blueprint', 'no-code', 'DAG authoring', 'configurations'. Good coverage of terms an Airflow user would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive niche combining Airflow task groups, Pydantic validation, YAML-based DAG composition, and no-code authoring. This is unlikely to conflict with other skills due to its very specific domain focus on Airflow DAG templating patterns.	3 / 3
	Total	12 / 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
skill_md_line_count	SKILL.md is long (503 lines); consider splitting into references/ and linking	Warning

	Total	10 / 11 Passed

Repository: astronomer/agents
Commit: 789b454

Reviewed: 8 days ago

Table of Contents

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