Build Amazon Machine Images (AMIs) with Packer using the amazon-ebs builder. Use when creating custom AMIs for EC2 instances.
92
88%
Does it follow best practices?
Impact
100%
1.23xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
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 with excellent trigger terms and clear when/what guidance. The main weakness is limited specificity in capabilities - it only mentions building AMIs without detailing other actions like template validation, provisioner configuration, or multi-region builds. The description effectively carves out a distinct niche.
Suggestions
Expand the capabilities list to include additional concrete actions like 'validate templates, configure provisioners, manage build variables'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AMIs, Packer, amazon-ebs builder) and one action (build/create), but doesn't list multiple concrete actions like configuring provisioners, validating templates, or managing build artifacts. | 2 / 3 |
Completeness | Clearly answers both what ('Build Amazon Machine Images with Packer using the amazon-ebs builder') and when ('Use when creating custom AMIs for EC2 instances') with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'AMI', 'AMIs', 'Packer', 'amazon-ebs', 'EC2 instances', 'custom AMIs'. These are terms users would naturally use when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche combining Packer + AMI + amazon-ebs builder. Unlikely to conflict with general AWS skills, Terraform skills, or other infrastructure tools due to the specific technology combination. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a high-quality skill that provides concrete, executable Packer templates for building AMIs. The content is concise, assumes Claude's competence, and includes practical examples for common use cases. The main weakness is the workflow section could more explicitly emphasize validation as a required checkpoint before building.
Suggestions
Restructure Build Commands section to emphasize validation as a required step: '1. Initialize: packer init . 2. **Validate (required)**: packer validate . 3. If errors: fix and re-validate 4. **Only when valid**: packer build .'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, providing only necessary information without explaining what Packer or AMIs are. Every section serves a purpose with no padding or unnecessary context. | 3 / 3 |
Actionability | Provides fully executable HCL templates that are copy-paste ready, concrete bash commands for building, and specific source AMI filters with actual owner IDs. All code examples are complete and runnable. | 3 / 3 |
Workflow Clarity | Build commands are listed but lack explicit validation checkpoints. The workflow (init -> validate -> build) is present but doesn't emphasize the validate step as a required checkpoint before building, and there's no feedback loop for handling validation failures. | 2 / 3 |
Progressive Disclosure | Well-organized with clear sections progressing from basic to advanced (multi-region). External references are clearly signaled at top and bottom. Content is appropriately structured for a single SKILL.md file without needing additional files. | 3 / 3 |
Total | 11 / 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
9f2ede9
Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.