terraform-patterns
**UTILITY SKILL** — Reusable Azure Terraform patterns: hub-spoke, private endpoints, diagnostics, AVM-TF modules. WHEN: "hub-spoke Terraform", "private endpoint module", "AVM-TF composition", "diagnostic settings", "plan interpretation". DO NOT USE FOR: Bicep code (azure-bicep-patterns), ADRs (azure-adr), diagrams (drawio).
75
92%
Does it follow best practices?
Impact
—
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 an excellent skill description that hits all the key criteria. It uses third person voice, provides specific concrete capabilities, includes natural trigger terms in a clear WHEN clause, and explicitly delineates boundaries with a DO NOT USE FOR section that names competing skills. The description is concise yet comprehensive, making it easy for Claude to select correctly from a large skill set.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete patterns and actions: hub-spoke, private endpoints, diagnostics, AVM-TF modules, and plan interpretation. These are clearly defined, actionable capabilities rather than vague abstractions. | 3 / 3 |
Completeness | Clearly answers both 'what' (reusable Azure Terraform patterns for hub-spoke, private endpoints, diagnostics, AVM-TF modules) and 'when' (explicit WHEN clause with trigger phrases). Also includes a 'DO NOT USE FOR' clause that further clarifies boundaries, which is excellent for disambiguation. | 3 / 3 |
Trigger Term Quality | Includes highly natural and specific trigger terms users would actually say: 'hub-spoke Terraform', 'private endpoint module', 'AVM-TF composition', 'diagnostic settings', 'plan interpretation'. These cover the key phrases a user working with Azure Terraform would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with explicit negative boundaries ('DO NOT USE FOR: Bicep code, ADRs, diagrams') that directly prevent conflicts with named sibling skills. The Azure Terraform niche with specific patterns like hub-spoke and AVM-TF is very clearly delineated. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%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 skill that serves as an effective hub for Azure Terraform patterns. Its strengths are excellent progressive disclosure with clear reference navigation, a well-sequenced workflow with validation checkpoints, and concise presentation that respects Claude's intelligence. The main weakness is that actionable code examples are deferred to reference files rather than shown inline, making the skill slightly less immediately executable.
Suggestions
Add a minimal but complete inline HCL snippet in the Canonical Example section (e.g., a resource group + key vault AVM module composition) rather than deferring entirely to references/module-composition.md — this would make the skill immediately actionable without loading additional files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient. It assumes Claude's competence with Terraform and Azure, avoids explaining basic concepts, and uses tables and terse bullet points. The note about canonical sources is brief and justified to prevent rule duplication. Every section earns its place. | 3 / 3 |
Actionability | The skill provides specific rules, commands (terraform plan, fmt, validate), and concrete gotchas with exact attribute renames. However, the main SKILL.md lacks executable HCL code examples inline — the canonical example section describes what to do but defers the actual code to references/module-composition.md. The Steps section gives concrete CLI commands but the core IaC patterns are described rather than shown. | 2 / 3 |
Workflow Clarity | The Steps section provides a clear 7-step sequence from pattern identification through validation, with explicit validation checkpoints (step 6: plan review, step 7: fmt/validate/security checks). The plan-before-apply step with specific flags and the multi-tool validation step constitute proper feedback loops for infrastructure operations. | 3 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure: a Quick Reference table maps patterns to reference files, the body stays concise with inline rules and gotchas, and a detailed Reference Index at the bottom provides clear one-level-deep navigation to 12 specific reference files. References are well-signaled and consistently formatted. | 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
05d7617
- Reviewed
Table of Contents
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