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azure-monitor-query-py

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.

53

Quality

60%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/azure-monitor-query-py/SKILL.md

The canonical home for this skill is azure-monitor-query-py in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

57%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a reasonably lean, code-heavy SDK cheat sheet covering logs, metrics, async, and Kusto queries. Its main weaknesses are a broken metrics example, no integrated validation in the batch flow, and a single monolithic file with no progressive disclosure.

Suggestions

Fix the 'Query Resource Metrics' code block: remove the duplicated metric_names/timespan/granularity lines and correct the indentation so the example is executable.

Integrate partial-result/error handling into the batch query example rather than presenting it only as a separate snippet, to add a validation checkpoint to a batch operation.

Move the Kusto query library and/or the extended API examples into a references file and link to it from the body, and replace the generic 'When to Use' boilerplate with concrete trigger guidance.

DimensionReasoningScore

Conciseness

The body is predominantly lean, code-forward content with little concept explanation, though the 'Best Practices' list rehashes the examples and the boilerplate 'When to Use' line is filler that could be trimmed.

4 / 5

Actionability

Most sections give concrete executable code, but the 'Query Resource Metrics' example is malformed (duplicated metric_names/timespan/granularity lines and broken indentation) and will not run, leaving a key gap.

3 / 5

Workflow Clarity

The skill is organized as a catalog of snippets rather than a sequenced workflow, and the batch query example lacks integrated validation of partial results; per the rubric, batch operations without validation cap this at 3.

3 / 5

Progressive Disclosure

The file has clear section headers but everything is inlined into a single ~250-line file with no external references, so content that could be split (e.g., the Kusto query library) is inline rather than progressively disclosed.

3 / 5

Total

13

/

20

Passed

Description

62%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is concise and names a clear, specific niche with two concrete actions and an explicit use clause. It would benefit from broader trigger-term synonyms and more concrete trigger scenarios in the 'when' portion.

Suggestions

Add common synonyms and trigger phrases such as 'KQL', 'Kusto Query Language', and 'Azure logs' to improve trigger-term coverage.

Expand the 'Use for' clause with concrete trigger scenarios (e.g., 'Use when the user asks to query Azure logs, run KQL queries, or read Azure Monitor metrics').

Consider naming additional concrete actions (batch queries, metric aggregations) to move specificity toward comprehensive coverage.

DimensionReasoningScore

Specificity

Names the domain ('Azure Monitor Query SDK for Python') and two concrete actions ('querying Log Analytics workspaces' and 'Azure Monitor metrics'), but stops at two actions without comprehensive coverage.

3 / 5

Completeness

Provides a clear 'what' and an explicit 'Use for ...' when clause, but the when clause restates the what rather than naming concrete trigger scenarios, so it is not fully explicit.

4 / 5

Trigger Term Quality

Includes relevant natural terms ('Azure Monitor', 'Log Analytics workspaces', 'metrics') but omits common synonyms a user might say such as 'KQL', 'Kusto', or 'Azure logs'.

3 / 5

Distinctiveness Conflict Risk

The Azure Monitor niche is specific and mostly distinct, though generic trigger words like 'metrics' create minor overlap risk with other metrics-related skills.

4 / 5

Total

14

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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