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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.

88

1.01x
Quality

82%

Does it follow best practices?

Impact

96%

1.01x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

A well-organized, concise overview that leans too heavily on a referenced playbook that is absent, leaving the body's guidance abstract. Adding a concrete code/example and a real validation checkpoint would materially raise quality.

Suggestions

Add at least one concrete, executable DAG example or operator/sensor code snippet so the body is actionable on its own, independent of the referenced playbook.

Replace the single 'Validate in staging' step with an explicit validate→fix→re-validate feedback loop, given the batch/production nature of the workflows.

Provide the missing resources/implementation-playbook.md (or remove the broken reference) so the progressive-disclosure navigation resolves to real content.

DimensionReasoningScore

Conciseness

Lean, well-sectioned body that assumes Claude's competence and avoids explaining concepts Claude already knows; the only slack is the playbook being referenced twice, which is minor.

3 / 3

Actionability

Instructions are abstract directives ('Design idempotent tasks', 'Implement DAGs with observability') with no concrete code, commands, or examples; the concrete material is deferred to a reference file.

2 / 3

Workflow Clarity

Steps are sequenced 1–4 and mention staging validation, but there is no explicit validate→fix→retry feedback loop, which the rubric requires for batch/production operations.

2 / 3

Progressive Disclosure

Sections are well organized and signal a one-level-deep reference to the playbook, but the referenced resources/implementation-playbook.md does not exist in the bundle, breaking navigation.

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.

A strong, third-person description that names concrete capabilities, supplies explicit 'Use when' triggers, and occupies a clear niche. No verbosity or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'operators, sensors, testing, and deployment' alongside building production DAGs — rather than vague language.

3 / 3

Completeness

Clearly states what it does ('Build production Apache Airflow DAGs with best practices...') and an explicit 'Use when' trigger clause covering both facets.

3 / 3

Trigger Term Quality

Natural terms users would say are well covered: 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs', 'Airflow', 'DAGs'.

3 / 3

Distinctiveness Conflict Risk

Scoped to Apache Airflow DAGs with distinct orchestration triggers, making it unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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