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.
52
57%
Does it follow best practices?
Impact
—
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), uses natural trigger terms users would employ, and includes an explicit 'Use when' clause. It is clearly scoped to Apache Airflow, making it highly distinctive and unlikely to conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: building DAGs, using operators, sensors, testing, and deployment. These are concrete, domain-specific capabilities rather than vague language. | 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 the primary terms a user would naturally use when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with 'Apache Airflow' and 'DAGs' as clear niche identifiers. Unlikely to conflict with other skills since it targets a specific orchestration framework with domain-specific terminology. | 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 stub that delegates 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 actionable guidance. The instructions are abstract platitudes that wouldn't help Claude build a production DAG.
Suggestions
Add at least one complete, executable DAG example showing a common pattern (e.g., a simple ETL pipeline with BashOperator/PythonOperator, proper default_args, and retry configuration).
Replace the abstract 4-step instructions with concrete, sequenced steps including specific commands (e.g., `airflow dags test my_dag 2024-01-01`) and validation checkpoints.
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') and use that token budget for actionable content like operator selection guidance, common pitfalls with code examples, and testing patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary sections like 'Use this skill when' and 'Do not use this skill when' which are meta-guidance Claude doesn't need spelled out at this length. The 'Limitations' section is boilerplate. However, it's not excessively verbose—just not lean. | 2 / 3 |
Actionability | The instructions are extremely vague and abstract ('Identify data sources, schedules, and dependencies', 'Design idempotent tasks'). There are no concrete code examples, no executable DAG snippets, no specific commands, and no copy-paste ready content. The skill describes rather than instructs. | 1 / 3 |
Workflow Clarity | The four instruction steps are high-level platitudes with no specific sequencing, no validation checkpoints, and no feedback loops. For a skill involving production DAG deployment and backfills (potentially destructive operations), the complete absence of concrete validation steps is a significant gap. | 1 / 3 |
Progressive Disclosure | The skill references `resources/implementation-playbook.md` twice, but no bundle files are provided, meaning the reference leads nowhere. The SKILL.md itself contains almost no substantive content—it's essentially an empty shell pointing to a non-existent file, which is worse than a monolithic wall of text. | 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
95574f3
- Reviewed
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