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.

1.01x

Quality

75%

Does it follow best practices?

Impact

96%

1.01x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./docs/v19.7/configuration/agent/skills_external/antigravity-awesome-skills-main/skills/airflow-dag-patterns/SKILL.md

Quality

Content

50%

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

This skill is concise but lacks actionability - it reads more like a table of contents than a skill file. The instructions are abstract guidance without concrete code examples, DAG templates, or specific commands. The heavy reliance on an external playbook file means the skill body itself provides minimal standalone value.

Suggestions

Add at least one complete, executable DAG example showing the recommended patterns (e.g., a simple ETL DAG with proper error handling and retries)

Replace abstract instructions like 'Design idempotent tasks' with concrete guidance: specific code patterns, configuration snippets, or command examples

Include a quick-start section with copy-paste ready code that demonstrates core concepts before pointing to the detailed playbook

Add validation commands or checks (e.g., 'airflow dags test', 'airflow tasks test') as explicit workflow checkpoints

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient, avoiding unnecessary explanations of what Airflow is or how DAGs work. Every section serves a purpose and assumes Claude's competence with the technology.	3 / 3
Actionability	The instructions are vague and abstract ('Identify data sources', 'Design idempotent tasks') with no concrete code examples, executable commands, or copy-paste ready DAG templates. It describes rather than instructs.	1 / 3
Workflow Clarity	Steps are listed in sequence (identify, design, implement, validate) but lack validation checkpoints, specific commands, or feedback loops for error recovery. The workflow is too high-level to guide actual implementation.	2 / 3
Progressive Disclosure	References to implementation-playbook.md are present and one-level deep, but the SKILL.md itself provides almost no actionable content - it's essentially just a pointer to another file with minimal overview value.	2 / 3
	Total	8 / 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 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 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 clear differentiators. Unlikely to conflict with general coding skills or other workflow tools due to specific technology focus.	3 / 3
	Total	12 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: duclm1x1/Dive-Ai
Commit: 20ba150

Reviewed: 4 months ago

Table of Contents

Discovery Implementation Validation

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