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.

1.09x

Quality

65%

Does it follow best practices?

Impact

92%

1.09x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/airflow-dag-patterns/SKILL.md

The canonical home for this skill is airflow-dag-patterns in rmyndharis/antigravity-skills

Quality

Content

47%Weight 40%Scale 1-5

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

The body is admirably concise but stops at abstract directives, providing no executable code, concrete operator names, or validation steps, and its only detailed reference points to a file that does not exist. It needs concrete examples and a real playbook to back its pointer.

Suggestions

Add at least one copy-paste-ready DAG code example (with a concrete operator, schedule, retries, and a sensor) so the guidance is executable rather than descriptive.

Provide an explicit validate->fix->retry checkpoint loop for batch/destructive operations (e.g., staging validation before promoting a DAG, and dry-run/limited-backfill verification before full backfills).

Create the referenced resources/implementation-playbook.md (or correct the path to a real bundle file) and signal it clearly once from the body so progressive disclosure resolves to actual content.

Dimension	Reasoning	Score
Conciseness	Lean and efficient with no padding and no explanation of concepts Claude already knows; every section earns its place and it assumes Claude's competence.	5 / 5
Actionability	Only high-level hints ('Design idempotent tasks with clear ownership and retries', 'Implement DAGs with observability and alerting hooks') with no executable code, specific operators, APIs, or commands; it describes rather than instructs.	2 / 5
Workflow Clarity	A rough 4-step sequence exists but it is purely abstract with no validation commands or checkpoints, and the skill governs destructive/batch operations (production schedules, backfills) where the missing validate->fix->retry loop caps the score.	2 / 5
Progressive Disclosure	The body references 'resources/implementation-playbook.md' (twice) but no resources directory or file exists, so the reference is broken; there is section structure but no real deferred material to navigate to.	2 / 5
	Total	11 / 20 Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, well-balanced description that clearly states both capabilities and natural use-when triggers, with good keyword coverage and a distinct Airflow niche. Minor gains are available in enumerating more granular concrete actions and adding tool-name/file-extension synonyms.

Dimension	Reasoning	Score
Specificity	Lists several specific concrete capability areas ('operators, sensors, testing, and deployment') beyond just naming the domain, though 'best practices' is somewhat abstract and the actions are topical areas rather than a fully enumerated action set.	4 / 5
Completeness	Explicitly 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 concrete trigger phrases.	5 / 5
Trigger Term Quality	'creating data pipelines, orchestrating workflows, or scheduling batch jobs' are natural phrases users say, but there is no file-extension or tool-name synonym coverage and the phrasing is slightly formal.	4 / 5
Distinctiveness Conflict Risk	A clear Apache Airflow niche with distinct triggers and minimal conflict risk, but 'scheduling batch jobs' has minor overlap with generic cron/scheduler skills.	4 / 5
	Total	17 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/airflow-dag-patterns/SKILL.md
Commit: 731a928

Reviewed: 10 hours ago

Table of Contents

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