Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling".
38
37%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/devops/auto-scaling-configurator/skills/configuring-auto-scaling-policies/SKILL.mdQuality
Discovery
40%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 description suffers from a lack of concrete actions and specificity about what the skill actually does. While it includes some trigger phrases and a 'Use when' clause, the core capability description is padded with vague terms like 'comprehensive guidance and automation' rather than listing specific actions. It would benefit greatly from specifying the platform/technology and enumerating concrete tasks.
Suggestions
Replace 'comprehensive guidance and automation' with specific concrete actions such as 'define scaling policies, set min/max instance counts, configure scaling triggers based on CPU/memory metrics, create launch templates'.
Specify the target platform or technology (e.g., AWS Auto Scaling Groups, Kubernetes HPA, Azure VMSS) to improve distinctiveness and help Claude select the right skill.
Add more natural trigger term variations users might say, such as 'scale up', 'scale down', 'horizontal scaling', 'scaling policy', 'ASG', or 'auto scale group'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description says 'auto-scaling configuration with comprehensive guidance and automation' but never lists concrete actions like 'define scaling policies', 'set min/max instance counts', or 'configure CloudWatch alarms'. The language is vague and abstract. | 1 / 3 |
Completeness | It has a 'Use when' clause ('when you need to work with auto-scaling') and trigger phrases, but the 'what does this do' portion is extremely weak — 'comprehensive guidance and automation' says almost nothing about actual capabilities. The 'when' is present but generic. | 2 / 3 |
Trigger Term Quality | It includes some relevant trigger phrases like 'configure auto-scaling', 'set up elastic scaling', and 'implement scaling', but misses common variations users might say such as 'scale up/down', 'horizontal scaling', 'ASG', 'scaling policy', or platform-specific terms like 'AWS Auto Scaling Group'. | 2 / 3 |
Distinctiveness Conflict Risk | Auto-scaling is a somewhat specific domain, but the description is so vague ('comprehensive guidance and automation') that it could overlap with general infrastructure, DevOps, or cloud configuration skills. It doesn't specify a platform (AWS, Azure, GCP, Kubernetes) which would help distinguish it. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a high-level checklist or planning document than an actionable skill for Claude. Its biggest weakness is the complete absence of executable code—no Terraform, no YAML, no CLI commands—despite claiming to generate configurations for four cloud platforms. The error handling table is a useful addition, but the skill fundamentally fails to provide the concrete, copy-paste-ready guidance that would make it effective.
Suggestions
Add at least one complete, executable example per platform (e.g., a full Terraform HCL block for AWS ASG target tracking, a complete HPA YAML manifest) instead of just listing output formats.
Replace the natural language 'Examples' section with actual input/output pairs showing a user request and the corresponding generated configuration.
Add explicit validation commands at key workflow steps (e.g., 'Run `aws autoscaling describe-scaling-activities --auto-scaling-group-name <name>` to verify policy attachment').
Consider splitting platform-specific guidance into separate referenced files (e.g., AWS_AUTOSCALING.md, K8S_HPA.md) to allow depth without bloating the main skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content includes some unnecessary verbosity—the Prerequisites section explains things Claude already knows (what CLI tools are, what IAM permissions are), and the Examples section lists natural language prompts rather than executable examples. However, the error handling table and instructions are reasonably efficient. | 2 / 3 |
Actionability | Despite covering four cloud platforms, the skill provides zero executable code, no Terraform HCL, no YAML manifests, no CLI commands. The instructions are entirely abstract ('Select scaling metric(s)', 'Configure cooldown periods') with no concrete, copy-paste-ready examples. The Output section lists what should be generated but never actually shows any of it. | 1 / 3 |
Workflow Clarity | Steps are listed in a logical sequence and step 9 mentions validation via load simulation, but there are no explicit validation checkpoints between steps, no feedback loops for error recovery during configuration, and the validation step is vague ('simulating load and confirming scaling events fire correctly') without concrete commands. | 2 / 3 |
Progressive Disclosure | The content is organized into clear sections (Overview, Prerequisites, Instructions, Output, Error Handling, Examples, Resources), which is decent structure. However, given the breadth of four cloud platforms, the content would benefit from splitting platform-specific guidance into separate files rather than keeping everything in one document with no depth in any area. | 2 / 3 |
Total | 7 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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Table of Contents
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