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

Discovery

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 Airflow task group templates, Pydantic validation, YAML-based DAG composition) and provides explicit trigger guidance via a 'Use when...' clause. It uses proper third-person voice and covers a distinctive niche that would be easily distinguishable from other skills. The description is concise yet comprehensive.

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 Airflow task group templates with Pydantic validation and compose DAGs from YAML) and 'when' (explicit 'Use when...' clause covering 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 templates', 'no-code DAG authoring', 'configurations'. Good coverage of terms a user working in this domain 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 technology stack and use case.	3 / 3
	Total	12 / 12 Passed

Implementation

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 for a single skill. The routing table and verification checklist are excellent structural choices that aid navigation and validation.

Suggestions

Split detailed sections (Customizing DAG-Level Configuration, Runtime Parameter Overrides, Versioning, Troubleshooting) into separate bundle files and reference them from the main SKILL.md to reduce token footprint.

Trim the 'Before Starting' section — Claude doesn't need to be told to confirm Airflow/Python versions every time; a single line noting the requirements is sufficient.

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 'Runtime Parameter Overrides' and 'Customizing DAG-Level Configuration' are thorough but could be tightened. The troubleshooting section is appropriately concise. However, the skill doesn't over-explain concepts Claude already knows (e.g., it doesn't explain what Airflow or Pydantic are).	2 / 3
Actionability	The skill provides fully executable, copy-paste ready code examples throughout — from project setup (loader.py, install commands), to blueprint class definitions, YAML configurations, CLI commands, and troubleshooting fixes. Every section includes concrete, specific guidance with real code rather than pseudocode or abstract descriptions.	3 / 3
Workflow Clarity	The skill has clear sequencing throughout: Project Setup is numbered 1-2-3, the routing table directs users to the right section, and the verification checklist at the end provides explicit validation checkpoints. The validation workflow section includes expected output. The troubleshooting section provides cause-fix pairs. The 'Before Starting' section establishes prerequisites before any work begins.	3 / 3
Progressive Disclosure	The content is well-organized with clear section headers and a routing table for navigation, but it's a monolithic document (~300 lines) with no references to external files. Several sections (Customizing DAG-Level Configuration, Runtime Parameter Overrides, Versioning) could be split into separate reference files to keep the main skill leaner. No bundle files are provided to offload detailed content.	2 / 3
	Total	10 / 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: 535a040

Reviewed: 14 days ago

Table of Contents

Discovery Implementation Validation

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