deploying-airflow

Deploy Airflow DAGs and projects. Use when the user wants to deploy code, push DAGs, set up CI/CD, deploy to production, or asks about deployment strategies for Airflow.

1.14x

Quality

77%

Does it follow best practices?

Impact

92%

1.14x

Average score across 3 eval scenarios

Securityby

Risky

Do not use without reviewing

Optimize this skill with Tessl

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

Quality

Discovery

89%

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 solid skill description that clearly communicates its purpose and when to use it. The explicit 'Use when...' clause with multiple trigger scenarios is well done, and the Airflow-specific focus provides good distinctiveness. The main area for improvement is adding more specific concrete actions beyond the general 'deploy' verb.

Suggestions

Add more specific concrete actions to improve specificity, e.g., 'configure CI/CD pipelines, sync DAG folders, manage deployment environments, set up automated testing for DAGs'.

Dimension	Reasoning	Score
Specificity	Names the domain (Airflow DAGs/projects) and mentions deployment-related actions (deploy code, push DAGs, set up CI/CD, deploy to production), but the actions are somewhat generic and not deeply specific about concrete capabilities like 'configure GitHub Actions pipelines' or 'sync DAG folders to S3'.	2 / 3
Completeness	Clearly answers both 'what' (deploy Airflow DAGs and projects) and 'when' with an explicit 'Use when...' clause listing multiple trigger scenarios including deploying code, pushing DAGs, CI/CD setup, production deployment, and deployment strategies.	3 / 3
Trigger Term Quality	Includes strong natural trigger terms users would actually say: 'deploy code', 'push DAGs', 'CI/CD', 'deploy to production', 'deployment strategies', 'Airflow'. These cover common variations of how users would phrase deployment requests.	3 / 3
Distinctiveness Conflict Risk	The combination of 'Airflow' + 'deployment/DAGs/CI/CD' creates a clear niche that is unlikely to conflict with general deployment skills or general Airflow development skills. The scope is well-bounded.	3 / 3
	Total	11 / 12 Passed

Implementation

64%

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

This is a comprehensive deployment guide with strong actionability — every section provides executable commands and complete configuration files. Its main weaknesses are length (inline YAML configurations inflate the document significantly) and the absence of validation/verification steps after deployment operations. The three deployment paths are well-organized but the skill would benefit from being split into overview + reference files.

Suggestions

Add explicit validation steps after each deployment path (e.g., 'Verify: curl http://localhost:8080/health', 'Check DAGs loaded: airflow dags list', 'If health check fails: check logs with...')

Extract the large Docker Compose YAML and Helm values.yaml into separate reference files, keeping only minimal examples inline with links to the full configurations

Remove redundant descriptions that repeat what the command tables already convey (e.g., the prose under 'Full Project Deploy' largely restates the table entry)

Dimension	Reasoning	Score
Conciseness	The skill is fairly well-organized but includes some unnecessary explanations (e.g., describing what each deploy command does when the table already covers it, explaining what Docker Compose services do). The Docker Compose YAML and Helm values.yaml are quite lengthy and could be trimmed, though they do provide actionable configuration. Some sections like 'Deploy Queue' add minimal value.	2 / 3
Actionability	The skill provides fully executable commands, complete Docker Compose YAML files, Helm chart configurations, and specific CLI commands throughout. Every section includes copy-paste ready code with concrete examples for each deployment path.	3 / 3
Workflow Clarity	While individual commands and configurations are clear, the skill lacks explicit validation checkpoints. For example, after deploying via Helm or Docker Compose, there are no verification steps (e.g., 'check health endpoint', 'verify DAGs loaded'). The 'Common Operations' section lists commands but doesn't sequence them into a validated workflow. For deployment operations that can fail silently, this is a notable gap.	2 / 3
Progressive Disclosure	The content is well-structured with clear sections (Astro, Docker Compose, Kubernetes) and a related skills section at the end. However, the skill is quite long (~250+ lines) with large inline YAML blocks that could be split into separate reference files. The Helm values.yaml and Docker Compose configurations are substantial enough to warrant their own files, with the SKILL.md providing a concise overview and links.	2 / 3
	Total	9 / 12 Passed

Validation

100%

Warnings & errors only

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

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: astronomer/agents
Commit: 535a040

Reviewed: 14 days ago

Table of Contents

Discovery Implementation Validation

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