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.

1.07x

Quality

71%

Does it follow best practices?

Impact

88%

1.07x

Average score across 6 eval scenarios

Securityby

Passed

No known issues

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

Evaluation results

76%

-10%

Daily Sales Report Pipeline

Production DAG default_args and retry configuration

Criteria

Without context

With context

retries=3

100%

retry_delay 5 min

100%

Exponential backoff

100%

max_retry_delay 1h

100%

email_on_failure True

email_on_retry False

100%

catchup=False

100%

max_active_runs=1

100%

No depends_on_past

100%

Task failure callback

100%

DAG failure callback

100%

No hardcoded date

100%

schedule= syntax

100%

Inventory Reconciliation Pipeline with Conditional Processing

Sensor patterns, branching, and trigger rules

Criteria

Without context

With context

TaskFlow @dag decorator

100%

TaskFlow @task decorator

100%

@task.sensor used

100%

Sensor mode=reschedule

100%

Sensor timeout set

100%

Sensor poke_interval set

100%

BranchPythonOperator used

100%

Join trigger rule

100%

Cleanup trigger rule

100%

PokeReturnValue returned

100%

95%

-1%

Multi-Tenant Report Pipeline with Tests

DAG testing, project structure, and dynamic generation

Criteria

Without context

With context

Factory function

100%

globals() registration

100%

Config-driven clients

100%

Logic in common module

100%

DAG file stays thin

100%

DagBag fixture

100%

include_examples=False

100%

Import error test

100%

Cycle detection test

71%

64%

Structural assertion

100%

80%

Daily Content Platform Event Aggregation

Incremental processing with lazy imports and no global state

Criteria

Without context

With context

context['ds'] usage

77%

{{ ds }} in templated field

Lazy imports inside tasks

100%

No top-level heavy imports

100%

TaskFlow @task decorator

100%

Date-partitioned output path

100%

catchup=False

100%

owner set

100%

16%

Financial Transaction Reconciliation Pipeline

S3KeySensor and ExternalTaskSensor configuration

Criteria

Without context

With context

S3KeySensor used

100%

S3 bucket_key uses {{ ds }}

100%

ExternalTaskSensor used

100%

execution_date_fn specified

100%

Sensor mode=reschedule

100%

Sensor timeout set

100%

Sensor poke_interval set

100%

Fan-in to processing task

100%

aws_conn_id specified

100%

80%

24%

Nightly Database Backup Pipeline with Monitoring

ALL_SUCCESS trigger rule and task dependency testing

Criteria

Without context

With context

TriggerRule.ALL_SUCCESS used

100%

90%

TriggerRule.ALL_DONE used

100%

upstream_list dependency check

100%

downstream_list dependency check

22%

DagBag include_examples=False

100%

Import errors assertion

100%

Cycle detection test

50%

Structural task count assertion

100%

Repository: wshobson/agents
Commit: 70444e5

Evaluated: 29 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Daily Sales Report Pipeline Inventory Reconciliation Pipeline with Conditional Processing Multi-Tenant Report Pipeline with Tests Daily Content Platform Event Aggregation Financial Transaction Reconciliation Pipeline Nightly Database Backup Pipeline with Monitoring

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