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.
56
62%
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 ./.github/plugins/azure-skills/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 strong skill description with excellent trigger term coverage and clear completeness via an explicit WHEN clause. Its main weakness is that the 'what' portion could be more specific about concrete actions beyond 'query and analyze data'. The distinctiveness is excellent due to the specific technology niche.
Suggestions
Expand the capability description with more specific concrete actions, e.g., 'Write and optimize KQL queries, build summarization pipelines, detect anomalies in time series, join across tables and clusters' to improve specificity.
| 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 multiple natural variations and use cases. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche around Azure Data Explorer/Kusto/KQL. The specific technology stack (ADX, KQL, Kusto) and use cases (IoT telemetry, log analytics) make it very unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is comprehensive in coverage but suffers from significant verbosity and redundancy. It explains concepts Claude already knows (what ADX is, what KQL is, use cases), includes unnecessary activation triggers, and duplicates best practices across sections. The KQL examples are a strength, being concrete and executable, but the MCP tool guidance lacks concrete invocation examples, and the workflow lacks validation checkpoints.
Suggestions
Remove the 'Skill Activation Triggers', 'Overview', 'Key Data Fields', 'Result Format', and 'Use Cases' sections entirely — Claude already knows these concepts and they consume tokens without adding actionable value.
Add concrete MCP tool invocation examples showing exact parameter usage, e.g., a complete `kusto_query` call with subscription, cluster, database, and query parameters filled in.
Merge the two 'Best Practices' sections into a single concise list, removing general advice Claude already knows (like 'monitor query performance').
Add a validation step to the Core Workflow, such as checking row counts or verifying non-empty results before proceeding to analysis.
| 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 section partially duplicates the Best Practices section. Overall, the skill could be cut by 50%+ without losing actionable value. | 1 / 3 |
Actionability | The KQL examples are concrete and executable, which is good. However, the MCP tool usage lacks concrete examples showing actual tool invocations with parameters (e.g., how to call `kusto_query` with specific arguments). The CLI fallback section provides a useful concrete command. The skill describes more than it instructs in many sections. | 2 / 3 |
Workflow Clarity | The 'Core Workflow' provides a 4-step sequence but lacks validation checkpoints. There's no explicit verification step after running queries (e.g., checking row counts, validating results). The fallback strategy section is well-structured with clear 'when to fallback' criteria, but the main workflow is too high-level and missing feedback loops for error recovery. | 2 / 3 |
Progressive Disclosure | No bundle files are provided, so all content is inline in a single monolithic file. The content is reasonably organized with clear headers, but the sheer volume (KQL patterns, best practices, CLI fallback, common issues, use cases) would benefit from being split into referenced files. For a skill of this length (~200 lines), the lack of any external references means the main file is overloaded. | 2 / 3 |
Total | 7 / 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
d02fd24
- Reviewed
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