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

78%

Does it follow best practices?

Impact

Pending

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

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 well-crafted skill description that excels across all dimensions. It provides specific capabilities (DAGs, operators, sensors, testing, deployment), includes natural trigger terms users would actually say, explicitly states both what it does and when to use it, and is clearly distinguishable from other skills through its Airflow-specific focus.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Build production Apache Airflow DAGs', 'operators', 'sensors', 'testing', and 'deployment'. These are concrete, actionable capabilities within the Airflow domain.	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 explicit trigger guidance.	3 / 3
Trigger Term Quality	Includes natural keywords users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs'. These cover common variations of how users describe workflow orchestration needs.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with 'Apache Airflow' and 'DAGs' as specific identifiers. Unlikely to conflict with general coding skills or other workflow tools due to explicit Airflow focus and domain-specific terminology.	3 / 3
	Total	12 / 12 Passed

Implementation

57%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is well-structured and concise, with appropriate progressive disclosure to detailed resources. However, it fails to provide any actionable, executable guidance in the main skill file itself—all concrete implementation details are deferred to external files, leaving the SKILL.md as an abstract outline rather than a useful quick reference.

Suggestions

Add at least one minimal, executable DAG code example showing the recommended pattern (e.g., a simple DAG with TaskFlow API or classic operators)

Include a concrete validation step in the workflow, such as 'Run `airflow dags test dag_id execution_date` to validate locally before deploying'

Provide specific examples of idempotent task patterns or retry configurations rather than abstract guidance

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient, avoiding unnecessary explanations of what Airflow is or how orchestration works. Every section serves a purpose without padding.	3 / 3
Actionability	The instructions are vague and abstract ('Identify data sources', 'Design idempotent tasks') with no concrete code examples, specific commands, or executable guidance. Everything actionable is deferred to an external file.	1 / 3
Workflow Clarity	Steps are listed in sequence (1-4) but lack validation checkpoints, feedback loops, or specific verification steps. The safety section mentions testing but doesn't integrate it into the workflow.	2 / 3
Progressive Disclosure	Clear overview structure with well-signaled one-level-deep references to implementation-playbook.md. Content is appropriately split between overview and detailed resources.	3 / 3
	Total	9 / 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: 332e58b

Reviewed: 17 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.