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/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 what the skill does (build Airflow DAGs with best practices across operators, sensors, testing, deployment) and when to use it (data pipelines, workflow orchestration, batch job scheduling). It uses third-person voice, includes highly relevant trigger terms, and is clearly distinguishable from other skills due to its Airflow-specific terminology.
| 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 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 an explicit 'Use when...' clause. | 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 | Clearly scoped to Apache Airflow specifically, with domain-specific terms like 'DAGs', 'operators', 'sensors' that are distinctive to Airflow. Unlikely to conflict with general coding or other workflow tools. | 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 bundle file that doesn't exist. The body contains no executable code, no concrete patterns, no specific Airflow API usage, and no validation steps. It reads more like a table of contents for a document that was never written.
Suggestions
Add at least one complete, executable DAG example (e.g., a minimal production DAG with retries, catchup=False, and proper default_args) directly in the SKILL.md.
Replace the abstract 4-step instructions with a concrete workflow including specific commands (e.g., 'airflow dags test my_dag 2024-01-01', validation steps, and error recovery guidance).
Either provide the referenced 'resources/implementation-playbook.md' bundle file or inline the essential patterns, checklists, and templates into the SKILL.md itself.
Remove the boilerplate 'Use this skill when / Do not use this skill when / Limitations' sections and replace them with actionable content like operator selection guidance, common pitfalls with code examples, and testing patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'Use this skill when' and 'Do not use this skill when' sections add moderate bloat—Claude can infer applicability from context. The limitations section is boilerplate. However, the instructions section itself is reasonably brief. | 2 / 3 |
Actionability | There are no concrete code examples, no executable DAG snippets, no specific commands, and no copy-paste ready patterns. The four instruction steps are high-level abstractions ('Design idempotent tasks', 'Implement DAGs with observability') that describe rather than instruct. All real content is deferred to a bundle file that doesn't exist. | 1 / 3 |
Workflow Clarity | The four numbered steps are vague directives without validation checkpoints, error recovery, or concrete sequencing. For a skill involving production deployments and backfills (destructive/batch operations), the absence of any validation or feedback loops 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. | 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
- sickn33/antigravity-awesome-skills
- Commit
45bad85
- Reviewed
Table of Contents
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