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

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npx tessl skill review --optimize ./skills/airflow-dag-patterns/SKILL.md

Evaluation results

98%

-2%

Daily Order Fulfilment Pipeline

Idempotent task design with retries and ownership

Criteria

Without context

With context

Data sources identified

100%

Schedule specified

100%

Dependencies documented

100%

Idempotent load task

100%

Retries configured

100%

Retry delay configured

100%

Task ownership assigned

100%

Backfill guidance in README

100%

80%

Three or more tasks

100%

Task dependency chain

100%

No plain blind INSERT

100%

90%

Financial Data Aggregation Pipeline with Monitoring

DAG observability and alerting hooks

Criteria

Without context

With context

Failure callback present

100%

Retry callback present

SLA configured

100%

SLA miss callback

100%

Task success callback

100%

Logging in task bodies

100%

Ownership set

100%

Retries configured

100%

Monitoring notes file

100%

Hourly schedule

100%

No silent failure path

100%

Production Deployment Preparation for a Marketing Attribution DAG

Staging validation and operational runbook

Criteria

Without context

With context

Staging validation file

100%

Staging environment mentioned

100%

Runbook exists

100%

Manual trigger instructions

100%

Backfill procedure documented

60%

100%

Backfill duplication warning

100%

Common failure modes

100%

Ownership added to DAG

100%

Retries added to DAG

100%

Alerting hook added

100%

No schedule change without note

100%

Repository: haniakrim21/everything-claude-code
Commit: ae2cadd

Evaluated: 18 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Daily Order Fulfilment Pipeline Financial Data Aggregation Pipeline with Monitoring Production Deployment Preparation for a Marketing Attribution DAG

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