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/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 tool (Apache Airflow), lists specific capabilities (DAGs, operators, sensors, testing, deployment), and provides explicit trigger guidance via a 'Use when' clause. The trigger terms are natural and cover the main use cases users would describe. Minor improvement could include mentioning file types or additional synonyms, but overall this is well-crafted.
| 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 rather than actionable guidance.
Suggestions
Add at least one complete, executable DAG example in the SKILL.md showing a basic production pattern (e.g., a simple ETL DAG with retries, catchup=False, and proper default_args).
Replace the abstract 4-step instructions with concrete, specific guidance—e.g., actual operator usage patterns, common pitfalls with specific code fixes, and key configuration settings.
Add validation checkpoints to the workflow, such as 'Run `airflow dags test <dag_id> <date>` to validate locally before deploying' and 'Check `airflow dags list-import-errors` after deployment'.
Provide a brief summary of what's in the implementation playbook so readers know what patterns are available without having to open the file (e.g., 'Covers: TaskFlow API patterns, custom operator templates, CI/CD pipeline config, monitoring setup').
| 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 lean but almost too sparse—they're high-level bullet points without substance. | 2 / 3 |
Actionability | The instructions are entirely abstract ('Identify data sources', 'Design idempotent tasks', 'Implement DAGs with observability') with no 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 rather than a clear workflow. There are no validation checkpoints, no feedback loops, and no specific commands or tools mentioned. For a skill involving production deployment and batch operations, the absence of verification steps is a significant gap. | 1 / 3 |
Progressive Disclosure | The skill references `resources/implementation-playbook.md` for detailed content, which is appropriate progressive disclosure. However, the SKILL.md itself provides almost no useful overview content—it's essentially an empty shell pointing to one external file, making it hard to know what's available without navigating away. | 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
- sickn33/antigravity-awesome-skills
- Commit
d739c8b
- Reviewed
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