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.
54
61%
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/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 a clear 'Use when' clause with natural trigger terms, and is highly distinctive due to Airflow-specific terminology. It uses proper third-person voice and avoids vague language or buzzwords.
| 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 abstractions. | 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 due to the specific mention of 'Apache Airflow', 'DAGs', 'operators', and 'sensors' — these are unique to the Airflow ecosystem and unlikely to conflict with other skills. The combination of these terms creates a clear niche. | 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 thin wrapper that defers all substantive content to a referenced file (`resources/implementation-playbook.md`) that doesn't exist in the bundle. The body itself provides no executable code, no concrete patterns, and only abstract high-level instructions. It fails to deliver on its promise of 'production-ready patterns' and would leave Claude with no actionable guidance.
Suggestions
Add at least one complete, executable DAG example (e.g., a minimal production DAG with proper default_args, retries, and a BashOperator or PythonOperator) directly in the SKILL.md.
Replace the abstract 4-step instructions with concrete, sequenced workflow 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, checklists, and templates into the SKILL.md itself.
Remove boilerplate sections ('Use this skill when', 'Do not use this skill when', 'Limitations') and use that token budget for actionable content like operator selection guidance, testing patterns, and common debugging commands.
| 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 without any concrete sequencing, validation checkpoints, or feedback loops. For a skill involving production DAG deployment and backfills (potentially destructive operations), the complete absence of validation steps is a significant gap. | 1 / 3 |
Progressive Disclosure | The skill references `resources/implementation-playbook.md` for detailed content, which is a reasonable progressive disclosure pattern. However, no bundle files are provided, meaning the referenced file doesn't exist, and the SKILL.md itself contains almost no substantive content—it's essentially an empty shell pointing to a missing file. | 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
d89c349
- Reviewed
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