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.
65
57%
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 concisely covers specific capabilities (DAGs, operators, sensors, testing, deployment), includes an explicit 'Use when' clause with natural trigger terms, and is clearly distinguishable from other skills through the Apache Airflow domain focus. It uses proper third-person voice and avoids vague language or buzzwords.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: building DAGs, working with operators, sensors, testing, and deployment. These are concrete, domain-specific capabilities rather than vague language. | 3 / 3 |
Completeness | Clearly answers both 'what' (build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment) and 'when' (explicit 'Use when' clause covering data pipelines, orchestrating workflows, or 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 the primary terms a user working with Airflow would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific mention of 'Apache Airflow' and 'DAGs', which clearly carve out a niche. Unlikely to conflict with general data engineering or other workflow tools since the technology is explicitly named. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
14%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 placeholder that defers all meaningful content to a referenced file (`resources/implementation-playbook.md`) that doesn't exist in the bundle. The body contains no executable code, no concrete examples, no specific Airflow patterns, and no real workflow guidance. The instructions are abstract phases that could apply to any orchestration tool, providing no Airflow-specific value.
Suggestions
Add at least one complete, executable DAG example demonstrating core patterns (e.g., a simple ETL DAG with BashOperator/PythonOperator, retries, and alerting).
Replace the abstract 4-step instructions with concrete, sequenced steps including specific commands (e.g., `airflow dags test my_dag 2024-01-01`, validation with `airflow dags list-import-errors`).
Either include the referenced `resources/implementation-playbook.md` in the bundle or inline the essential patterns directly in the SKILL.md body.
Remove boilerplate sections ('Use this skill when', 'Limitations') that don't add Airflow-specific value, and use that space for actionable content like operator selection guidance, common pitfalls, or testing patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'Use this skill when' and 'Do not use this skill when' sections are somewhat verbose and explain things Claude can infer. The Limitations section is boilerplate. However, the Instructions section is lean. Overall, there's noticeable padding but it's not egregiously verbose. | 2 / 3 |
Actionability | The instructions are entirely abstract ('Identify data sources', 'Design idempotent tasks', 'Implement DAGs with observability'). There are no concrete code examples, no executable DAG definitions, no specific commands, and no copy-paste ready content. All substantive guidance is deferred to a resource file that doesn't exist in the bundle. | 1 / 3 |
Workflow Clarity | The four instruction steps are vague high-level phases rather than a clear actionable workflow. There are no validation checkpoints, no feedback loops, and no concrete sequencing. For a skill involving production deployments and batch operations, the absence of explicit validation steps is a significant gap. | 1 / 3 |
Progressive Disclosure | The skill references `resources/implementation-playbook.md` twice as the source of all detailed content, but this file is not provided in the bundle. The SKILL.md body itself contains almost no substantive content, making it an empty shell pointing to a non-existent resource. This is worse than a monolithic wall of text because there's nothing actionable at any level. | 1 / 3 |
Total | 5 / 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
431bfad
- Reviewed
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