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devops-automator

Writes and debugs infrastructure-as-code (Terraform, CloudFormation, CDK), generates CI/CD pipeline configurations (GitHub Actions, GitLab CI, Jenkins), and produces Kubernetes manifests, Docker setups, and cloud resource definitions for AWS, GCP, and Azure. Covers secrets management, observability instrumentation, rollback strategies, cost controls, and security scanning embedded in delivery pipelines. Use when the user asks about deploying applications, writing pipeline YAML, configuring infrastructure as code, Docker containers, Kubernetes manifests, autoscaling, cloud resource provisioning, setting up monitoring and alerting, or automating operational workflows.

90

Quality

88%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Discovery

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.

This is an excellent skill description that thoroughly covers the DevOps and infrastructure-as-code domain. It lists specific tools and platforms, concrete actions, and provides a comprehensive 'Use when' clause with natural trigger terms. The description is detailed without being padded, uses proper third-person voice throughout, and would be highly distinguishable from other skills in a large skill library.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions across several domains: writing/debugging Terraform/CloudFormation/CDK, generating CI/CD configs for specific platforms, producing Kubernetes manifests, Docker setups, and cloud resource definitions. Also covers secrets management, observability, rollback strategies, cost controls, and security scanning.

3 / 3

Completeness

Clearly answers both 'what' (writes/debugs IaC, generates CI/CD configs, produces K8s manifests, covers secrets/observability/rollback/cost/security) and 'when' with an explicit 'Use when...' clause listing specific trigger scenarios like deploying applications, writing pipeline YAML, configuring infrastructure as code, etc.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms users would say: Terraform, CloudFormation, CDK, GitHub Actions, GitLab CI, Jenkins, Kubernetes, Docker, AWS, GCP, Azure, pipeline YAML, autoscaling, monitoring, alerting, infrastructure as code, CI/CD. These are all terms users would naturally use when requesting DevOps/infrastructure help.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear DevOps/infrastructure niche with highly specific triggers (Terraform, CloudFormation, Kubernetes, CI/CD pipelines, cloud provisioning). Unlikely to conflict with general coding skills or other domain-specific skills due to the specificity of the infrastructure and deployment terminology.

3 / 3

Total

12

/

12

Passed

Implementation

77%

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

This is a strong, actionable skill with excellent concrete patterns covering Terraform, GitHub Actions, and Kubernetes. The workflow includes proper validation and rollback steps throughout. The main weaknesses are moderate verbosity in the workflow/constraints sections with some guidance Claude already knows, and the lack of progressive disclosure to separate files for the many additional topics mentioned in the skill description (CDK, CloudFormation, GitLab CI, Jenkins, observability configs).

Suggestions

Extract advanced topics (observability configs, CloudFormation/CDK patterns, GitLab CI/Jenkins examples) into referenced sub-files to improve progressive disclosure and reduce main file length.

Trim the workflow section to remove general advice Claude already knows (e.g., 'assess application topology', 'agree on naming conventions') and focus on DevOps-specific decision points.

DimensionReasoningScore

Conciseness

The skill is mostly efficient with good code examples, but the workflow section contains some general advice that Claude already knows (e.g., 'assess application topology', 'agree on naming conventions'). The constraints section also includes some obvious best practices. Could be tightened by ~20%.

2 / 3

Actionability

Provides fully executable, copy-paste-ready code examples across Terraform, GitHub Actions, and Kubernetes. Each pattern includes specific commands (terraform plan, kubectl rollout undo), concrete configurations with real resource definitions, and practical details like Trivy scanning integration.

3 / 3

Workflow Clarity

The 7-step workflow is clearly sequenced with explicit validation checkpoints: terraform plan before apply, health check after deploy with automatic rollback on failure, and post-rollout review. Each code pattern includes its own validation step, and the GitHub Actions example demonstrates a complete feedback loop (health check → rollback).

3 / 3

Progressive Disclosure

The content is well-structured with clear sections (Workflow, Concrete Patterns, Constraints), but it's a fairly long monolithic file with no references to external files for advanced topics like observability configs, multi-cloud variations, or CDK/CloudFormation examples mentioned in the description. Some content could be split out.

2 / 3

Total

10

/

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
OpenRoster-ai/awesome-agents
Reviewed

Table of Contents

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