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.

Quality

77%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

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 a strength. The main area for improvement is adding more specific concrete actions beyond the general 'deploy' verb to better differentiate the depth of capability.

Suggestions

Add more specific concrete actions to improve specificity, e.g., 'configure CI/CD pipelines, sync DAG folders, manage environment variables, 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, setting up CI/CD, and asking about 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 to deployment of Airflow specifically.	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 solid, actionable deployment guide with excellent concrete examples and copy-paste ready configurations across three deployment paths (Astro, Docker Compose, Kubernetes). Its main weaknesses are the lack of validation/verification steps after deployment operations and the large inline configuration blocks that make the document lengthy. Adding deployment verification workflows and splitting large configs into referenced files would significantly improve it.

Suggestions

Add explicit validation steps after each deployment method (e.g., 'Verify: curl http://localhost:8080/health should return healthy', 'kubectl get pods -n airflow — all pods should be Running')

Move the full docker-compose.yaml and values.yaml into separate referenced files (e.g., 'See [docker-compose-local.yaml](docker-compose-local.yaml) for the full config') to reduce inline bulk

Add a troubleshooting or error recovery section for common deployment failures (e.g., image build failures, pod CrashLoopBackOff, database migration issues)

Dimension	Reasoning	Score
Conciseness	The skill is fairly comprehensive but includes some unnecessary verbosity—e.g., explaining what each Airflow service does (scheduler, triggerer, etc.) which Claude already knows, and the lengthy docker-compose.yaml and values.yaml could be trimmed. However, most content is relevant deployment-specific configuration that adds value.	2 / 3
Actionability	The skill provides fully executable, copy-paste ready commands and configuration files throughout—complete docker-compose.yaml, Helm install commands, Dockerfile examples, and CLI commands. Every section gives concrete, specific guidance.	3 / 3
Workflow Clarity	While individual commands and configurations are clear, there are no explicit validation checkpoints or feedback loops. For example, after deploying via Helm or Docker Compose, there's no 'verify the deployment succeeded' step, no health check validation sequence, and no error recovery guidance. For deployment operations (which can be destructive), this is a notable gap.	2 / 3
Progressive Disclosure	The content is well-structured with clear sections and tables, but it's quite long (~250 lines of substantive content) with large inline YAML blocks that could be referenced as separate files. The 'Related Skills' section at the end is good, but the Helm values.yaml and docker-compose.yaml could be split into reference files to keep the main skill leaner.	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: 166c98a

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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