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.
73
66%
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-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 clear distinctiveness for the Azure Data Explorer/KQL domain. The explicit 'WHEN:' clause ensures Claude knows when to select this skill. The main weakness is that the 'what' portion could be more specific about concrete actions beyond 'query and analyze data'.
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 parse structured logs.'
| 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' with an explicit 'WHEN:' clause listing trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms: 'KQL queries', 'Kusto database queries', 'Azure Data Explorer', 'ADX clusters', 'log analytics', 'time series data', 'IoT telemetry', 'anomaly detection' — these are terms users would naturally use when needing this skill. | 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 niche is clearly defined. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides strong actionable content with executable KQL examples and a well-documented MCP tool interface plus CLI fallback. However, it is significantly bloated with content Claude already knows (what ADX is, activation triggers, use cases), duplicated best practices sections, and lacks any progressive disclosure—everything is crammed into one large file. Trimming redundancy and splitting reference material into linked files would substantially improve this skill.
Suggestions
Remove the 'Skill Activation Triggers', 'Overview' explanation paragraph, 'Key Data Fields' generic descriptions, and 'Use Cases' section—Claude already knows these concepts and they waste tokens.
Merge the duplicated 'KQL Best Practices' and 'Best Practices' sections into a single concise section, keeping only non-obvious guidance.
Extract the CLI fallback reference, common functions list, and common issues into separate linked files (e.g., CLI_FALLBACK.md, KQL_REFERENCE.md) to improve progressive disclosure.
Add a validation step in the core workflow, such as checking query result row count or verifying cluster connectivity before proceeding to complex queries.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Significant verbosity throughout: the 'Skill Activation Triggers' section lists obvious triggers Claude can infer, the 'Overview' paragraph explains what Azure Data Explorer is (Claude already knows), 'Key Data Fields' describes generic field patterns, 'Use Cases' at the end is entirely unnecessary, and the 'Best Practices' section largely duplicates the 'KQL Best Practices' section. Many sections add padding without new actionable information. | 1 / 3 |
Actionability | The skill provides concrete, executable KQL examples for multiple query patterns, a clear MCP tools table with required/optional parameters, and a complete Azure CLI fallback with copy-paste-ready commands including the REST API call. The code examples are specific and executable. | 3 / 3 |
Workflow Clarity | The 'Core Workflow' provides a clear 4-step sequence, and the fallback strategy includes explicit conditions for when to switch. However, there are no validation checkpoints—no step to verify query results, check for errors before proceeding, or validate that the cluster/database connection succeeded before running queries. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files. At ~200+ lines, sections like the full CLI fallback reference, detailed KQL best practices, common functions list, and use cases could be split into separate reference files. Everything is inlined with no navigation structure beyond flat headings. | 1 / 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
742d20b
- Reviewed
Table of Contents
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