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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.

72

1.09x
Quality

61%

Does it follow best practices?

Impact

92%

1.09x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

22%Scale 1-3

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

This skill is essentially a stub that defers all substantive content to an external playbook file. The SKILL.md body contains no executable code, no concrete examples, and no specific Airflow patterns despite claiming to cover DAG design, operators, sensors, testing, and deployment. The instructions read as generic project management advice rather than actionable technical guidance.

Suggestions

Add at least one complete, executable DAG example in the SKILL.md showing a basic production pattern (e.g., a simple ETL DAG with retries, idempotency, and alerting).

Replace the abstract 4-step instructions with concrete, specific guidance—e.g., actual operator usage patterns, sensor configurations, and common pitfalls with code examples.

Add a validation/testing workflow with explicit checkpoints, such as 'Run `airflow dags test <dag_id> <date>` to validate locally before deploying' with error recovery steps.

Include a quick-start section with a minimal working DAG that demonstrates the key best practices, so the skill provides immediate value without requiring the external playbook.

DimensionReasoningScore

Conciseness

The content is relatively brief but includes some unnecessary sections like 'Use this skill when' and 'Do not use this skill when' which are more about routing than actionable instruction. The actual instructions are only 4 vague lines, so the signal-to-noise ratio is poor.

2 / 3

Actionability

The instructions are entirely abstract ('Identify data sources', 'Design idempotent tasks', 'Implement DAGs with observability') with zero concrete code, commands, or executable examples. There are no DAG code snippets, no operator examples, no configuration patterns—everything actionable is deferred to an external file.

1 / 3

Workflow Clarity

The four numbered steps are high-level platitudes without any specific sequencing, validation checkpoints, or feedback loops. For a skill involving production deployment and batch operations, there are no verification steps, no error recovery guidance, and no concrete workflow.

1 / 3

Progressive Disclosure

The skill references `resources/implementation-playbook.md` for detailed content, which is appropriate progressive disclosure. However, the SKILL.md itself provides almost no useful overview content—it's essentially an empty shell pointing to one file, with no quick-start material or concrete examples to orient the reader.

2 / 3

Total

6

/

12

Passed

Description

100%Scale 1-3

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

This is a strong skill description that clearly identifies the domain (Apache Airflow), lists specific capabilities (DAGs, operators, sensors, testing, deployment), and provides explicit trigger guidance via a 'Use when' clause. It uses proper third-person voice and includes natural keywords that users would actually say when needing this skill.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: building DAGs, working with operators, sensors, testing, and deployment. These are concrete, domain-specific capabilities rather than vague abstractions.

3 / 3

Completeness

Clearly answers both 'what' (build production Airflow DAGs with best practices for operators, sensors, testing, deployment) and 'when' (explicit 'Use when' clause covering data pipelines, orchestrating workflows, scheduling batch jobs).

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs', 'operators', 'sensors'. These cover the primary terms a user working with Airflow would naturally use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive due to the specific mention of 'Apache Airflow', 'DAGs', 'operators', and 'sensors' — these are unique to the Airflow ecosystem and unlikely to conflict with other skills like general Python scripting or other workflow tools.

3 / 3

Total

12

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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