azure-diagnostics
Debug and troubleshoot production issues on Azure. Covers Container Apps and Function Apps diagnostics, log analysis with KQL, health checks, and common issue resolution for image pulls, cold starts, health probes, and function invocation failures. USE FOR: debug production issues, troubleshoot container apps, troubleshoot function apps, troubleshoot Azure Functions, analyze logs with KQL, fix image pull failures, resolve cold start issues, investigate health probe failures, check resource health, view application logs, find root cause of errors, function app not working, function invocation failures DO NOT USE FOR: deploying applications (use azure-deploy), creating new resources (use azure-prepare), cost optimization (use azure-cost-optimization)
Install with Tessl CLI
npx tessl i github:microsoft/github-copilot-for-azure --skill azure-diagnostics91
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillEvaluation — 100%
↑ 1.58xAgent success when using this skill
Validation for skill structure
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 excels across all dimensions. It provides specific capabilities, comprehensive trigger terms covering both technical and casual user language, explicit 'USE FOR' and 'DO NOT USE FOR' guidance, and clear boundaries that distinguish it from related Azure skills. The description is well-structured and uses appropriate third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Container Apps and Function Apps diagnostics, log analysis with KQL, health checks, and common issue resolution for image pulls, cold starts, health probes, and function invocation failures.' These are detailed, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what (diagnostics, log analysis, health checks, issue resolution) AND when with explicit 'USE FOR:' triggers. Also includes 'DO NOT USE FOR:' guidance which helps Claude distinguish this skill from related ones. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'debug production issues', 'troubleshoot container apps', 'analyze logs with KQL', 'fix image pull failures', 'function app not working', 'find root cause of errors'. Includes both technical terms and casual phrasing like 'not working'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche (Azure production debugging) and explicit boundary markers via 'DO NOT USE FOR:' clause that references specific alternative skills (azure-deploy, azure-prepare, azure-cost-optimization). This prevents conflicts with related Azure 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 diagnostic skill with strong actionability and progressive disclosure. The main weaknesses are some unnecessary verbosity in the header/triggers sections and missing validation checkpoints in the diagnostic workflow. The reference architecture and concrete commands make it highly usable.
Suggestions
Remove or condense the 'AUTHORITATIVE GUIDANCE' header and 'Triggers' section - Claude can infer when to use this skill from context
Add explicit validation/verification steps to the Quick Diagnosis Flow (e.g., 'Confirm resource health status before proceeding to logs', 'Verify log query returns expected time range')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient but includes some unnecessary elements like the verbose 'AUTHORITATIVE GUIDANCE' header and the 'Triggers' section which restates what Claude would infer from context. The diagnostic flow and commands are appropriately concise. | 2 / 3 |
Actionability | Provides concrete, executable CLI commands and MCP tool invocations with clear parameter structures. Commands are copy-paste ready with placeholder patterns (RG, APP, RESOURCE_ID) that are easy to substitute. | 3 / 3 |
Workflow Clarity | The Quick Diagnosis Flow provides a clear sequence but lacks explicit validation checkpoints or feedback loops. For troubleshooting workflows involving production systems, there should be verification steps after each diagnostic action to confirm findings before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview, clear table routing to service-specific guides, and well-signaled one-level-deep references to detailed documentation (container-apps/, functions/, kql-queries.md). Navigation is intuitive. | 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.
- Reviewed
Table of Contents
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