azure-bicep-patterns
Reusable Azure Bicep patterns: hub-spoke, private endpoints, diagnostics, AVM composition. USE FOR: Bicep template design, hub-spoke networking, private endpoint patterns, AVM modules. DO NOT USE FOR: Terraform code, architecture decisions, troubleshooting, diagram generation.
89
86%
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 Bicep patterns, includes natural trigger terms that infrastructure engineers would use, and explicitly delineates both positive and negative use cases. The 'DO NOT USE FOR' clause is a particularly effective addition that reduces ambiguity and conflict risk with related skills like Terraform or architecture decision tools.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete patterns and actions: hub-spoke, private endpoints, diagnostics, AVM composition, and Bicep template design. These are concrete, domain-specific capabilities rather than vague abstractions. | 3 / 3 |
Completeness | Clearly answers both 'what' (reusable Azure Bicep patterns for hub-spoke, private endpoints, diagnostics, AVM composition) and 'when' (explicit 'USE FOR' clause with trigger scenarios, plus a 'DO NOT USE FOR' exclusion list that further clarifies scope). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Bicep', 'hub-spoke networking', 'private endpoint', 'AVM modules', 'Bicep template'. These are terms Azure infrastructure engineers would naturally use. The 'DO NOT USE FOR' clause also helps with negative triggers like 'Terraform'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche in Azure Bicep infrastructure patterns. The explicit 'DO NOT USE FOR' clause (Terraform, architecture decisions, troubleshooting, diagram generation) actively reduces conflict risk with adjacent skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%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 excels at progressive disclosure and conciseness, serving as an effective overview/index for Azure Bicep patterns. Its main weakness is that actionability relies heavily on reference files — the main skill body provides only one executable code example. Adding a brief deployment workflow with explicit validation steps would strengthen the workflow clarity dimension.
Suggestions
Add 1-2 more inline executable Bicep snippets for the most common patterns (e.g., diagnostic settings, private endpoint wiring) so the skill body itself is more actionable without requiring reference file lookups.
Add an explicit numbered deployment workflow with validation checkpoints, e.g.: 1. Write/update modules → 2. Run `az deployment group what-if` → 3. Review output for red flags → 4. Fix issues and re-run what-if → 5. Only when clean: deploy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It uses tables for quick reference, provides a single canonical example, and summarizes key rules as bullet points without explaining basic Azure or Bicep concepts Claude already knows. Every section earns its place. | 3 / 3 |
Actionability | The canonical module interface example is concrete and executable, but most guidance is summarized as terse bullet-point rules rather than fully executable code snippets. Key patterns like diagnostics, private endpoints, and conditional deployment lack inline code examples, deferring entirely to reference files. | 2 / 3 |
Workflow Clarity | The What-If section mentions running validation before every deploy and watching for red flags, but there's no explicit step-by-step workflow with validation checkpoints and feedback loops. For destructive/batch IaC operations, the lack of a clear deploy sequence with validate-fix-retry caps this at 2. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure: a concise overview with quick reference table, key rules summary, and gotchas inline, with all detailed patterns clearly linked to one-level-deep reference files. The Reference Index and Learn More tables provide clear navigation. | 3 / 3 |
Total | 10 / 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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