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.

0.95x

Quality

—

Does it follow best practices?

Impact

90%

0.95x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Content

65%

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

The body delivers highly actionable, executable Airflow patterns but is a long monolithic catalog that mixes reference material into SKILL.md without offloading detail to bundle files. Workflow sequencing and validation are only partially explicit since it is structured as a pattern library.

Suggestions

Move the longer patterns (e.g. dynamic DAG generation, sensors, error handling) into separate reference files under references/ and link to them from SKILL.md so the main file stays a lean overview.

Trim the 'When to Use This Skill' list and the DAG Design Principles table, which restate the description and concepts Claude already knows, to recover token budget.

Add an explicit production workflow sequence (develop → test with the provided pytest suite → validate DAG load → deploy) with validation checkpoints to lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean code with little explanatory prose, but at ~520 lines across six full DAG patterns plus a 'When to Use' list that duplicates the description, it could be tightened; the DAG Design Principles table also restates general data-engineering concepts Claude already knows.	2 / 3
Actionability	Every pattern is a complete, executable DAG file with imports, real operator usage, and copy-paste-ready code, including a pytest testing pattern — fully concrete guidance.	3 / 3
Workflow Clarity	Patterns are clearly labeled and the testing section provides a validation step, but the skill is a pattern catalog rather than a sequenced build→validate→deploy workflow, and there is no explicit validation checkpoint for the production/deployment operations it covers.	2 / 3
Progressive Disclosure	Sections are well-organized, but with no bundle files (references/, scripts/, assets/ absent) all six patterns and the project-structure detail live inline in one ~520-line SKILL.md; content that could be split into one-level-deep reference files is not.	2 / 3
	Total	9 / 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.

The description is concise, third-person, and cleanly separates capability from trigger conditions with explicit 'Use when' guidance. It names a distinct niche and concrete actions without fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'operators, sensors, testing, and deployment' — tied to building production Airflow DAGs, matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both what ('Build production Apache Airflow DAGs...') and when ('Use when creating data pipelines...'), matching the top anchor with an explicit trigger clause.	3 / 3
Trigger Term Quality	'creating data pipelines, orchestrating workflows, or scheduling batch jobs' are natural phrases a user would say, with good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	The 'Apache Airflow DAGs' niche with its specific trigger terms is clearly distinguishable and unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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
skill_md_line_count	SKILL.md is long (526 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: Dicklesworthstone/pi_agent_rust
Commit: abece4a

Reviewed: 4 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.