terraform-patterns
Reusable Azure Terraform patterns: hub-spoke, private endpoints, diagnostics, AVM-TF modules. USE FOR: Terraform template design, hub-spoke networking, AVM modules, plan interpretation. DO NOT USE FOR: Bicep code, architecture decisions, troubleshooting, diagram generation.
95
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
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 a strong skill description that clearly defines its scope with specific Azure Terraform capabilities, includes natural trigger terms practitioners would use, and explicitly delineates both positive and negative use cases. The DO NOT USE FOR clause is particularly effective at reducing conflict risk with related skills like Bicep or architecture decision skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete patterns and actions: hub-spoke, private endpoints, diagnostics, AVM-TF modules, Terraform template design, plan interpretation. These are concrete, domain-specific capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (reusable Azure Terraform patterns for hub-spoke, private endpoints, diagnostics, AVM-TF modules) and 'when' (USE FOR clause with explicit triggers). The DO NOT USE FOR clause adds further clarity on boundaries, serving as explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Terraform', 'hub-spoke', 'private endpoints', 'AVM modules', 'Azure', 'diagnostics', 'plan interpretation'. These are terms practitioners naturally use when working in this domain. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Azure Terraform patterns specifically. The DO NOT USE FOR clause explicitly excludes Bicep, architecture decisions, troubleshooting, and diagram generation, which sharply reduces conflict risk with adjacent skills. | 3 / 3 |
Total | 12 / 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 well-structured, token-efficient skill that serves as an effective pattern catalog for Azure Terraform. Its strengths are excellent progressive disclosure with clear navigation to 12 reference files, actionable code examples, and concise gotchas with specific mitigations. The main weakness is the lack of explicit workflow sequencing or validation checkpoints for applying these patterns in combination, though this is partially mitigated by the pattern-catalog nature of the skill.
Suggestions
Consider adding a brief workflow section (3-5 steps) showing the recommended order for applying patterns when scaffolding a new project (e.g., 1. scaffold → 2. hub-spoke → 3. private endpoints → 4. validate plan → 5. apply), with a validation checkpoint referencing the plan-interpretation reference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every section is lean and purposeful. No unnecessary explanations of what Terraform, Azure, or modules are. The quick reference table, key rules as bullet points, and gotchas are all high-signal content that Claude wouldn't already know. | 3 / 3 |
Actionability | The canonical example provides executable HCL code with clear annotations (the output wiring comment). Key rules are specific and prescriptive (e.g., '~> 4.0' pin, 'for_each over count', exact renamed attributes). Gotchas include concrete mitigations. | 3 / 3 |
Workflow Clarity | The skill is primarily a pattern reference rather than a multi-step workflow, but it lacks explicit sequencing for how to compose these patterns together (e.g., order of operations when setting up hub-spoke + private endpoints + diagnostics). The key rules and gotchas are clear but there are no validation checkpoints or feedback loops for applying these patterns. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear quick reference table mapping patterns to reference files, one inline canonical example, and a comprehensive reference index. All references are one level deep and clearly signaled with descriptive content summaries. | 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.
- Repository
- jonathan-vella/azure-agentic-infraops
- Commit
ec7b8ff
- Reviewed
Table of Contents
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