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

61%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agent/skills/airflow-dag-patterns/SKILL.md

Quality

Content

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 table of contents with no actionable content in the body itself. It defers all substance to referenced files that aren't provided, leaving the SKILL.md with only vague, abstract instructions. A production Airflow skill should include at minimum a concrete DAG example, specific operator usage patterns, and validation steps for deployment.

Suggestions

Add a concrete, executable 'Quick Start' DAG example (e.g., a minimal DAG with BashOperator or PythonOperator) directly in the SKILL.md so Claude has immediately actionable guidance.

Replace the abstract 4-step instructions with specific, sequenced workflow steps including validation checkpoints (e.g., 'Run `airflow dags test <dag_id> <date>` to validate locally before deploying').

Include at least one concrete pattern for common operations like retry configuration, idempotency enforcement, or sensor usage with actual code rather than descriptions.

Ensure referenced bundle files (resources/implementation-playbook.md, sub-skills/implementation-playbook.md) actually exist and contain the detailed content the SKILL.md promises.

Dimension	Reasoning	Score
Conciseness	The 'Use this skill when' and 'Do not use this skill when' sections add moderate bloat—Claude doesn't need to be told when Airflow is relevant. The instructions section is brief but the overall file has unnecessary framing that could be trimmed.	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 commands, no specific DAG patterns, and no copy-paste ready snippets. All substantive content is deferred to a referenced file that isn't provided.	1 / 3
Workflow Clarity	The four numbered steps are high-level platitudes without any specifics, validation checkpoints, or error recovery guidance. 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` and a sub-skill, which is a reasonable structure. However, no bundle files are provided, so the references cannot be verified. The SKILL.md itself contains almost no substantive content—it's essentially an empty shell pointing elsewhere, which means the overview itself fails to provide a useful quick-start.	2 / 3
	Total	6 / 12 Passed

Description

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 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: Dokhacgiakhoa/antigravity-ide
Commit: c4a059b

Reviewed: 5 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.