Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is lean, actionable, and well-structured with real one-level-deep references. The main gap is workflow clarity: the policy-application sequence lacks an explicit validation checkpoint wired into the flow.
Suggestions
Add an explicit validation step to the "Apply AI Governance Policy" sequence (e.g., a numbered step to call the Test AI Endpoint and confirm a 200/expected response before relying on the policy).
Wire the existing "Test AI Endpoint" block into the backend-add and policy-apply workflows as a verify-then-proceed checkpoint so errors are caught early.
Cross-link the troubleshooting fixes (e.g., token-limit 429, backend 401) inline at the relevant policy steps so recovery guidance appears where failures occur.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — quick-reference tables, copy-paste az/curl snippets, and short section prose — assuming Claude's competence and avoiding explanation of concepts Claude already knows, so every token earns its place. | 3 / 3 |
Actionability | It provides fully executable `az apim`/`curl` commands with concrete policy names and parameters, plus a quick-reference policy table, making the guidance copy-paste ready. | 3 / 3 |
Workflow Clarity | The "Apply AI Governance Policy" section lists a clear 1-6 sequence, but validation/checkpoint steps are only implicit (the separate "Test AI Endpoint" block is not wired into the flow as an explicit verify step), so it sits at the listed-but-gaps anchor rather than level 3. | 2 / 3 |
Progressive Disclosure | SKILL.md is a well-organized overview with clearly signaled one-level-deep references (policies.md, patterns.md, troubleshooting.md, sdk/) that are all real files with matching section anchors, splitting detail appropriately. | 3 / 3 |
Total | 11 / 12 Passed |