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.
70
62%
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 ./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 with multiple 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 combination of these terms creates a clear, unique niche. | 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.
The skill provides a broad reference for Azure Data Explorer querying with useful KQL examples and a well-structured fallback strategy. However, it is significantly bloated with content Claude already knows (what ADX is, activation triggers, use case lists), duplicates advice across sections, and lacks the tight, executable workflow guidance that would make it highly actionable. Trimming to ~40% of current length while adding concrete MCP tool invocation examples would substantially improve it.
Suggestions
Remove the 'Skill Activation Triggers', 'Overview', 'Key Data Fields', 'Result Format', and 'Use Cases' sections entirely—Claude already knows what ADX is and can infer when to use the skill from context.
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) rather than just listing parameters in a table.
Merge the duplicated 'KQL Best Practices' and 'Best Practices' sections into a single concise section focused only on non-obvious guidance.
Add validation checkpoints to the Core Workflow, such as verifying schema before querying and checking row counts after query execution to catch empty or unexpectedly large result sets.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Significant verbosity throughout. The 'Skill Activation Triggers' section is unnecessary (Claude can infer when to use the skill from context). The 'Overview' section explains what Azure Data Explorer is, which Claude already knows. 'Key Data Fields', 'Result Format', 'Use Cases' sections add little actionable value. The 'Best Practices' section largely duplicates 'KQL Best Practices'. Many sections describe rather than instruct. | 1 / 3 |
Actionability | KQL examples are concrete and executable, and the MCP tools table with parameters is useful. However, the skill lacks complete executable workflows showing how to chain MCP tool calls together (e.g., actual tool invocation syntax). The CLI fallback commands are concrete but the main MCP tool usage is described abstractly rather than with copy-paste-ready invocation examples. | 2 / 3 |
Workflow Clarity | The 'Core Workflow' provides a clear 4-step sequence, and the fallback strategy section has clear trigger conditions. However, there are no validation checkpoints—no guidance on verifying query results, checking for partial data, or handling errors mid-workflow. The workflow is more of a high-level outline than a detailed sequence with feedback loops. | 2 / 3 |
Progressive Disclosure | Content is organized into logical sections with headers, but everything is in a single monolithic file. Several sections (KQL Best Practices, Common Functions reference, Use Cases list) could be split into referenced files. No external file references are used despite the content length warranting them. | 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/github-copilot-for-azure
- Commit
a46a937
- Reviewed
Table of Contents
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