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.
68
61%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-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 explicit trigger guidance via a 'Use when' clause. It uses proper third-person voice and covers natural user language well, making it easy for Claude to select this skill appropriately.
| 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 trigger terms 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' and 'DAGs', which are unique to this orchestration tool. Unlikely to conflict with general coding or other pipeline skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
22%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a stub that defers all substantive content to an external playbook file. The SKILL.md body contains no executable code, no concrete examples, and no specific Airflow patterns despite claiming to cover DAG design, operators, sensors, testing, and deployment. The instructions are abstract platitudes that don't teach Claude anything it doesn't already know.
Suggestions
Add a concrete quick-start DAG example with executable Python code showing a minimal production-ready DAG pattern (imports, default_args, task definitions, dependencies).
Replace the abstract 4-step instructions with specific, actionable guidance — e.g., show how to implement idempotent tasks, configure retries with code, set up alerting callbacks.
Include at least one concrete operator/sensor example (e.g., PythonOperator, ExternalTaskSensor) with copy-paste ready code.
Add validation steps with specific commands (e.g., `airflow dags test <dag_id> <date>`, `airflow db check`) to provide workflow clarity for testing and deployment.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is relatively brief but includes some unnecessary sections like 'Use this skill when' and 'Do not use this skill when' which are meta-guidance Claude doesn't need. The actual instructions are very thin, with most content being organizational boilerplate rather than actionable knowledge. | 2 / 3 |
Actionability | The instructions are entirely abstract ('Identify data sources', 'Design idempotent tasks', 'Implement DAGs with observability') with zero concrete code, commands, or executable examples. There are no DAG code snippets, no operator examples, no configuration patterns — everything actionable is deferred to an external file. | 1 / 3 |
Workflow Clarity | The four numbered steps are vague high-level phases ('Identify', 'Design', 'Implement', 'Validate') without any specific validation checkpoints, commands, or feedback loops. For a skill involving production deployment and batch operations, the absence of concrete verification steps is a significant gap. | 1 / 3 |
Progressive Disclosure | There is a reference to `resources/implementation-playbook.md` which is one level deep and clearly signaled, but the SKILL.md itself provides almost no useful overview content — it's essentially an empty shell pointing to another file. A good overview should contain at minimum a quick-start example before deferring to detailed resources. | 2 / 3 |
Total | 6 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- boisenoise/skills-collections
- Commit
636b862
- Reviewed
Table of Contents
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