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.
90
88%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
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 communicates specific capabilities (reusable Airflow task group templates, Pydantic validation, YAML-based DAG composition) and provides explicit trigger guidance via a 'Use when...' clause with four distinct scenarios. The description is concise, uses third person voice, and occupies a clear niche that would be easily distinguishable from 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 template/DAG composition patterns. | 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, actionable skill with excellent executable examples and clear workflow guidance for the Blueprint system. Its main weakness is that it's a lengthy single document that could benefit from splitting advanced topics into separate files for better progressive disclosure. The routing table at the top adds some unnecessary verbosity but does help with navigation.
Suggestions
Split advanced topics (Runtime Parameter Overrides, Post-Build Callbacks, Versioning, Schema Generation, Troubleshooting) into separate referenced files to improve progressive disclosure and reduce the main skill's token footprint.
Remove or condense the 'Determine What the User Needs' routing table — Claude can infer user intent without an explicit lookup table, and this adds ~20 lines of overhead.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally well-structured but includes some unnecessary verbosity, such as the routing table at the top (Claude can determine what the user needs without an explicit lookup table), and some explanatory text that could be trimmed. However, it mostly avoids explaining concepts Claude already knows. | 2 / 3 |
Actionability | The skill provides fully executable code examples throughout — complete Python classes, bash commands, YAML configurations, and CLI invocations. Every section gives copy-paste ready guidance with specific imports, class structures, and command-line usage. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced (Project Setup has numbered steps, validation workflow is explicit, versioning has clear progression). The verification checklist at the end provides validation checkpoints, and the troubleshooting section covers error recovery. The 'Before Starting' confirmation step and final verification checklist create a good feedback loop. | 3 / 3 |
Progressive Disclosure | The content is well-organized with clear sections and a routing table, but it's a long monolithic document (~300+ lines) that could benefit from splitting advanced topics (Runtime Parameter Overrides, Post-Build Callbacks, Versioning, Schema Generation) into separate referenced files. Internal cross-references exist (e.g., 'see Customizing DAG-Level Configuration') but all content is inline. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
90%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
166c98a
- Reviewed
Table of Contents
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