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.

Quality

57%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/antigravity-bundle-data-engineering/skills/airflow-dag-patterns/SKILL.md

Quality

Content

14%Scale 1-3

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 defers all meaningful content to a referenced file (`resources/implementation-playbook.md`) that does not exist in the bundle. The body contains no executable code, no concrete examples, no specific Airflow patterns, 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 in the SKILL.md body (e.g., a minimal production DAG with a PythonOperator, retry config, and alerting callback).

Replace the abstract instruction steps with concrete, sequenced commands (e.g., `airflow dags test my_dag 2024-01-01` for local validation) and include explicit validation checkpoints before production deployment.

Either provide the referenced `resources/implementation-playbook.md` bundle file or inline the essential patterns directly in the SKILL.md.

Remove boilerplate sections (Limitations, 'Use this skill when') and replace 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 are somewhat verbose and explain things Claude can infer. The Limitations section is boilerplate. However, the Instructions section is reasonably lean.	2 / 3
Actionability	The instructions are entirely abstract ('Identify data sources', 'Design idempotent tasks', 'Implement DAGs with observability') with zero concrete code, commands, or executable examples. Everything actionable is deferred to a bundle file that doesn't exist.	1 / 3
Workflow Clarity	The four instruction steps are vague high-level phases with no specific commands, validation checkpoints, or feedback loops. For a skill involving production DAG deployment (a potentially destructive operation), the lack of any concrete validation steps is a significant gap.	1 / 3
Progressive Disclosure	The skill references `resources/implementation-playbook.md` but no bundle files are provided, meaning the reference leads nowhere. The SKILL.md itself contains almost no substantive content, making it an empty shell pointing to a non-existent resource.	1 / 3
	Total	5 / 12 Passed

Description

100%Scale 1-3

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 clearly identifies the technology (Apache Airflow), lists specific capabilities (DAGs, operators, sensors, testing, deployment), and provides an explicit 'Use when' clause with natural trigger terms. It is concise, uses third-person voice, and is highly distinguishable from other skills.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: building DAGs, using operators, sensors, testing, and deployment. Also mentions best practices, which adds practical context.	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 trigger terms users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs', 'operators', 'sensors'. These cover common variations of how users would describe Airflow-related tasks.	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 like general Python scripting or other orchestration tools.	3 / 3
	Total	12 / 12 Passed

Validation

90%

Warnings & errors only

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: popey/claude-code-skills
Path: plugins/antigravity-bundle-data-engineering/skills/airflow-dag-patterns/SKILL.md
Commit: a5a6601

Reviewed: 5 days ago

Table of Contents

Discovery Implementation Validation

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