airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
82
71%
Does it follow best practices?
Impact
88%
1.07xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/data-engineering/skills/airflow-dag-patterns/SKILL.mdQuality
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 identifies the technology (Apache Airflow), lists specific capabilities (DAGs, operators, sensors, testing, deployment), and provides an explicit 'Use when' clause with natural trigger terms. It is concise, uses third-person voice, and is highly distinguishable from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: building DAGs, using operators, sensors, testing, and deployment. Also mentions best practices, which adds practical context. | 3 / 3 |
Completeness | Clearly answers both 'what' (build production Airflow DAGs with best practices for operators, sensors, testing, deployment) and 'when' (explicit 'Use when' clause covering data pipelines, orchestrating workflows, scheduling batch jobs). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs', 'operators', 'sensors'. These cover common variations of how users would describe Airflow-related tasks. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific mention of 'Apache Airflow', 'DAGs', 'operators', and 'sensors' — these are unique to the Airflow ecosystem and unlikely to conflict with other skills like general Python scripting or other orchestration tools. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, production-quality Airflow code examples covering a comprehensive range of patterns. However, it is far too verbose for a SKILL.md—most of this content should be split into referenced files, and the main file should be a lean overview. The lack of an explicit development/deployment workflow with validation checkpoints is a notable gap.
Suggestions
Reduce SKILL.md to a concise overview with Quick Start and a brief summary of each pattern, moving full pattern code into separate referenced files (e.g., patterns/taskflow.py, patterns/dynamic_dags.py).
Remove the Core Concepts table and task dependency syntax section—Claude already knows these fundamentals and can infer them from the code examples.
Add an explicit development workflow with validation checkpoints: write DAG → run `pytest tests/test_dags.py` → validate with `airflow dags test <dag_id> <date>` → deploy.
Cut the 'When to Use This Skill' section—this is metadata that belongs in frontmatter, not body content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~400+ lines, with many patterns that are largely boilerplate code Claude already knows how to write. The 'Core Concepts' table explains basic principles (idempotent, atomic) that Claude understands, and the task dependency syntax section is unnecessary. Six full patterns with complete code examples is excessive for a SKILL.md. | 1 / 3 |
Actionability | All code examples are fully executable, copy-paste ready Python with proper imports, realistic configurations, and complete DAG definitions. The testing patterns include concrete pytest fixtures and assertions. Every pattern is immediately usable. | 3 / 3 |
Workflow Clarity | Individual patterns are clear and well-sequenced, but there's no overarching workflow for creating/deploying a DAG (e.g., write → test → validate → deploy). The testing section exists but isn't integrated into a development workflow with explicit validation checkpoints before deployment. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with no references to external files. All six patterns, the project structure, testing, and best practices are inlined. The content would benefit enormously from splitting patterns into separate files with a concise overview in SKILL.md pointing to them. | 1 / 3 |
Total | 7 / 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 (520 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- wshobson/agents
- Commit
27a7ed9
- Reviewed
Table of Contents
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