CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

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.

64

Quality

80%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/data-engineering/skills/airflow-dag-patterns/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%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 a well-disciplined overview with an executable Quick Start, an explicit one-level-deep reference to a substantial details file, and terse best-practice lists. Its weaknesses are pedagogical padding of Airflow basics Claude already knows and the absence of an explicit, validated workflow (no test/verify checkpoints) for what is a batch-operation domain.

Suggestions

Cut or compress the dependency-syntax block and the Core Concepts principles table — standard Airflow basics Claude already knows — and let references/details.md carry them if needed.

Add a short numbered workflow (define DAG → implement tasks → run tests → deploy) with an explicit validation step, e.g. `pytest tests/test_dags.py` or `airflow dags test`, which would also lift the workflow_clarity cap.

Itemize the reference pointer, e.g. "Pattern 4 (sensors) and Pattern 6 (testing) are in references/details.md", so readers can navigate without opening the file blind.

DimensionReasoningScore

Conciseness

The dependency-syntax block ("task1 >> task2", fan-out/fan-in) and the Core Concepts table re-teach standard Airflow basics Claude already knows, and the "When to Use This Skill" list duplicates the frontmatter description. Quick Start and the Do's/Don'ts are lean, so this sits at 'mostly efficient but includes some unnecessary explanation' rather than the minor-instances anchor above.

3 / 5

Actionability

The Quick Start is a complete, copy-paste-ready DAG (real default_args, schedule, retries) and the Do's/Don'ts cite specifics like "mode='reschedule'" and "{{ ds }}" macros. It falls short of fully-executable because extract_data is a placeholder ("# Extract logic here") and testing appears only as a bullet with no inline example.

4 / 5

Workflow Clarity

The body is organized by topic rather than as a sequenced build→test→deploy process, and there are no validation or verification checkpoints. Since DAGs are batch operations, the batch-operations cap applies: sequence is present implicitly but checkpoints are missing, which is the anchor-3 description and rules out 4.

3 / 5

Progressive Disclosure

The bundle matches the body's claim: references/details.md exists (409 lines, six worked patterns) and is one level deep, explicitly signaled with a read-condition ("Read that file when the navigation tier above is insufficient"), with the bulk detail correctly split out of a lean SKILL.md. The pointer does not itemize which patterns live in the file, so navigation requires opening it, keeping this at 'good structure, minor organization gaps' rather than the fully-navigable anchor.

4 / 5

Total

14

/

20

Passed

Description

88%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 description: it states concrete capabilities in third person and pairs them with an explicit, multi-phrase "Use when" trigger clause. Trigger coverage is good but misses natural synonyms like ETL or data engineering, and the generic orchestration/scheduling triggers carry minor conflict risk with broader scheduling skills.

DimensionReasoningScore

Specificity

"Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment" enumerates multiple concrete capability areas (operators, sensors, testing, deployment) with no notable coverage gaps for the domain, matching the comprehensive-coverage anchor rather than the 'minor gaps' anchor below it.

5 / 5

Completeness

The description explicitly answers both parts: the what ("Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment") and an explicit "Use when" clause with concrete trigger phrases, exactly matching the top anchor.

5 / 5

Trigger Term Quality

Triggers "creating data pipelines, orchestrating workflows, or scheduling batch jobs" plus "Apache Airflow" and "DAGs" are natural user phrasings, but common synonyms such as ETL, data engineering, or cron-style scheduling are absent, so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

"Apache Airflow DAGs" establishes a clear niche, but the when-triggers "orchestrating workflows" and "scheduling batch jobs" are generic enough to minorly overlap with general scheduling or orchestration skills, fitting the mostly-distinct anchor rather than the minimal-conflict one.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

wshobson/agents

Path

plugins/data-engineering/skills/airflow-dag-patterns/SKILL.md

Reviewed

Table of Contents

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.