azure-diagnostics
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors.
83
78%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.github/plugins/azure-skills/skills/azure-diagnostics/SKILL.mdQuality
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 (Azure production debugging), lists specific tools and actions, and provides an extensive WHEN clause with natural trigger terms covering a wide range of Azure-specific failure scenarios. The description is well-structured, uses third person voice, and would be easily distinguishable from other skills in a large skill library.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and tools: AppLens, Azure Monitor, resource health, safe triage, and references specific scenarios like crashloop, pod pending, node not ready, upgrade failures, cold start issues, health probe failures, and KQL analysis. | 3 / 3 |
Completeness | Clearly answers both 'what' (debug Azure production issues using AppLens, Azure Monitor, resource health, and safe triage) and 'when' with an explicit 'WHEN:' clause listing extensive trigger scenarios covering container apps, functions, AKS, specific error conditions, and log analysis. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'debug production issues', 'troubleshoot container apps', 'kubectl cannot connect', 'CoreDNS failures', 'pod pending', 'crashloop', 'node not ready', 'image pull failures', 'cold start issues', 'health probe failures', 'KQL', 'analyze logs'. These are highly natural phrases a user would type when encountering Azure production problems. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Azure-specific production debugging using Azure-native tools (AppLens, Azure Monitor, KQL). The combination of Azure platform + specific services (AKS, Container Apps, Functions) + specific failure modes (CoreDNS, crashloop, cold start) makes it very unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is well-structured as a routing/overview document with good progressive disclosure to service-specific guides. Its main weaknesses are unnecessary verbosity (authoritative banner, generic triggers list, basic diagnosis flow Claude already knows) and incomplete actionability—MCP tool syntax is ambiguous and CLI commands use bare placeholders without context. Adding validation checkpoints to the diagnostic workflow would strengthen it for production incident handling.
Suggestions
Remove the 'AUTHORITATIVE GUIDANCE' banner, the generic 'Triggers' section, and the 'Quick Diagnosis Flow' (steps like 'What's failing?' add no value for Claude) to improve conciseness.
Make MCP tool examples more concrete by showing a realistic invocation with a sample resource ID and expected output structure, or clarify the exact syntax Claude should use.
Add explicit validation/verification steps to the diagnostic workflow, e.g., 'After remediation, re-check resource health and confirm error rate drops in App Insights before closing the incident.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE' banner is unnecessary filler. The 'Triggers' section largely restates what Claude can infer from context. The 'Quick Diagnosis Flow' is generic knowledge Claude already has. However, the reference tables, CLI commands, and MCP tool examples are efficient and earn their tokens. | 2 / 3 |
Actionability | CLI commands and MCP tool invocations are concrete and useful, but several are incomplete (e.g., placeholder RESOURCE_ID without guidance on how to obtain it). The MCP tool examples use a pseudo-syntax that isn't clearly executable. The Quick Diagnosis Flow is abstract ('What's failing?') rather than actionable. | 2 / 3 |
Workflow Clarity | The Quick Diagnosis Flow provides a sequence but lacks validation checkpoints and feedback loops. For production debugging—a high-stakes, multi-step process—there's no explicit 'verify fix worked' step, no error recovery guidance, and no escalation path if diagnostics are inconclusive. The routing table is clear, however. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, well-organized routing table pointing to service-specific guides, and one-level-deep references to KQL queries, container apps, functions, and AKS troubleshooting docs. Navigation is easy and references are clearly signaled. | 3 / 3 |
Total | 9 / 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
- microsoft/azure-skills
- Commit
9d594ab
- Reviewed
Table of Contents
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