azure-kusto
Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.
53
58%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugin/skills/azure-kusto/SKILL.mdQuality
Discovery
89%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 solid skill description with excellent trigger term coverage and a clear WHEN clause that makes it easy for Claude to select appropriately. The main weakness is that the 'what' portion could be more specific about concrete actions beyond 'query and analyze data.' The distinctiveness is excellent given the specificity of the technology domain.
Suggestions
Expand the capability description with more specific actions, e.g., 'Write and optimize KQL queries, perform joins across tables, create summarizations, detect anomalies in time series data, and troubleshoot query performance.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Azure Data Explorer/Kusto/KQL) and some actions ('query and analyze data'), but doesn't list multiple specific concrete actions like creating queries, joining tables, building dashboards, or specific KQL operations. | 2 / 3 |
Completeness | Clearly answers both 'what' (query and analyze data in Azure Data Explorer using KQL for log analytics, telemetry, and time series analysis) and 'when' (explicit WHEN clause listing trigger scenarios like KQL queries, Kusto database queries, ADX clusters, etc.). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'KQL queries', 'Kusto database', 'Azure Data Explorer', 'ADX clusters', 'log analytics', 'time series data', 'IoT telemetry', 'anomaly detection' — these cover the major variations a user might use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — Azure Data Explorer, KQL, Kusto, and ADX are very specific technologies unlikely to conflict with other skills. The combination of these terms creates a clear, unique niche. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is overly verbose, spending many tokens on content Claude already knows (what ADX is, activation triggers, use case lists, general best practices). The KQL query patterns are the strongest section, providing concrete executable examples. However, the skill would benefit greatly from trimming redundant/obvious content, adding concrete tool invocation examples in the workflow, and splitting reference material into separate files.
Suggestions
Remove the 'Skill Activation Triggers', 'Overview', 'Key Data Fields', 'Result Format', and 'Use Cases' sections entirely—Claude already knows what ADX is and when to use this skill.
Merge the two overlapping best practices sections ('KQL Best Practices' and 'Best Practices') into a single concise section focused only on non-obvious guidance.
Make the Core Workflow actionable by showing actual tool invocations (e.g., `kusto_cluster_list` with parameters) rather than abstract step descriptions.
Split the CLI fallback reference, common KQL functions, and detailed query patterns into separate referenced files to improve progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Significant verbosity throughout. The 'Skill Activation Triggers' section is unnecessary (Claude doesn't need to be told when to activate). The 'Overview' section explains what Azure Data Explorer is, which Claude already knows. 'Key Data Fields', 'Result Format', 'Use Cases', and much of 'Best Practices' are general knowledge padding. The 'KQL Best Practices' and 'Best Practices' sections overlap substantially. The file is well over 150 lines with much content that doesn't earn its place. | 1 / 3 |
Actionability | The KQL examples are concrete and executable, and the MCP tool table with parameters is useful. However, the CLI fallback query example uses placeholders rather than a fully worked example, and the workflow is described abstractly ('Discover Resources', 'Explore Schema') without showing the actual tool invocations or concrete command sequences to execute each step. | 2 / 3 |
Workflow Clarity | The 'Core Workflow' provides a clear 4-step sequence, and the fallback strategy section adds useful error recovery guidance. However, the workflow lacks explicit validation checkpoints—there's no step to verify query results are reasonable, no feedback loop for iterating on queries that return unexpected results, and the core workflow steps are abstract rather than concrete tool invocations. | 2 / 3 |
Progressive Disclosure | All content is in a single monolithic file with no references to supporting files. The KQL best practices, common functions reference, use cases, and detailed CLI fallback commands could all be split into separate reference files. For a skill this long (~200 lines), the lack of any content splitting or navigation structure is a significant weakness. | 1 / 3 |
Total | 6 / 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/github-copilot-for-azure
- Commit
915f809
- Reviewed
Table of Contents
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