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.
84
Quality
78%
Does it follow best practices?
Impact
92%
1.09xAverage score across 3 eval scenarios
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 follows best practices. It uses third person voice, lists specific capabilities within the Airflow ecosystem, includes a clear 'Use when...' clause with natural trigger terms, and is distinctive enough to avoid conflicts with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Build production Apache Airflow DAGs', 'operators', 'sensors', 'testing', and 'deployment'. These are concrete, actionable capabilities within the Airflow domain. | 3 / 3 |
Completeness | Clearly answers both what ('Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment') AND when ('Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs'. These cover common variations of how users describe workflow orchestration needs. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with 'Apache Airflow' and 'DAGs' as clear differentiators. Unlikely to conflict with general coding skills or other workflow tools due to specific technology naming. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is well-structured and concise with good progressive disclosure to external resources, but critically lacks actionable content. The instructions are abstract guidance rather than concrete, executable patterns - there are no code examples, DAG templates, or specific commands that would enable Claude to actually implement Airflow DAGs.
Suggestions
Add at least one complete, executable DAG example showing the recommended patterns (e.g., a simple ETL DAG with proper error handling and retries)
Include specific operator examples with code snippets (e.g., PythonOperator, BashOperator, sensor patterns)
Add concrete validation commands for testing DAGs locally (e.g., `airflow dags test dag_id execution_date`)
Replace abstract instructions like 'Design idempotent tasks' with specific patterns showing how to achieve idempotency in code
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, avoiding unnecessary explanations of what Airflow is or how DAGs work. Every section serves a purpose and assumes Claude's competence with the technology. | 3 / 3 |
Actionability | The instructions are vague and abstract ('Identify data sources', 'Design idempotent tasks') with no concrete code examples, executable commands, or copy-paste ready DAG templates. It describes rather than instructs. | 1 / 3 |
Workflow Clarity | Steps are listed in sequence (identify, design, implement, validate) but lack validation checkpoints, specific commands, or feedback loops for error recovery. The workflow is too high-level to guide actual implementation. | 2 / 3 |
Progressive Disclosure | Clear overview structure with well-signaled one-level-deep reference to implementation-playbook.md. Content is appropriately split between overview and detailed patterns in a separate resource file. | 3 / 3 |
Total | 9 / 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
5c5ae21
- Reviewed
Table of Contents
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