dt-dql-essentials

Core DQL syntax, pitfalls, query patterns, and query optimization. Load to write, build, fix, or OPTIMIZE a DQL query — prevents syntax errors and makes queries faster, more efficient, and cheaper (less data scanned = lower query consumption/cost per run). Covers fetch commands, data models, field namespaces, time alignment, entity/smartscape patterns, metric discovery, and performance/cost optimization (filter early, bucket filters, short time ranges, field selection, sampling, cardinality). Trigger: "write/build/fix a DQL query", "DQL syntax", "query logs/spans/metrics", "create a timeseries", "optimize my DQL", "make my query faster/cheaper", "reduce DQL cost/consumption/scanned data", "keep DQL cost under control". Do NOT use to explain an existing query or answer product questions. For MONITORING a tenant's ACTUAL query consumption/billing (how much queries cost, who scanned most, cost trends) use dt-platform-costs — this tunes the query text, not billing data.

1.27x

Quality

93%

Does it follow best practices?

Impact

65%

1.27x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Quality

Content

86%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.

A high-quality, executable reference skill with excellent progressive disclosure and a strong pitfalls table. The main flaw is a duplicated makeTimeseries section that wastes tokens and slightly muddies navigation.

Suggestions

Remove the duplicate '## makeTimeseries Command' section (lines 372–397) — it repeats the one at lines 293–312; merge any unique content (the spread: lifetime example, the dql-commands.md link) into the first instance.

Add a one-line 'How to use this skill' workflow at the top (identify task → consult pitfalls table → load the matched reference → write query) to make the routing sequence explicit rather than implied by the table.

Dimension	Reasoning	Score
Conciseness	The body is dense and assumes Claude's competence (no padded explanations of what a query language is), but the '## makeTimeseries Command' section is duplicated verbatim at lines 293 and 372, wasting tokens — efficient with one notable trim opportunity, matching the 'minor instances that could be trimmed' anchor rather than the lean-every-token-earns-its-place anchor.	4 / 5
Actionability	Copy-paste-ready DQL snippets throughout (fetch with samplingRatio, makeTimeseries, chained lookup, timeseries rollup) plus a wrong-vs-right pitfalls table giving executable corrections, matching the 'fully executable, copy-paste ready, covers common cases' anchor.	5 / 5
Workflow Clarity	The 'When to Load References' table gives a clear task→reference routing sequence and the Reference Index routes function groups to specs; no destructive/batch operations exist that would require validation checkpoints, so the destructive-cap does not apply, but it lacks the explicit feedback loops of the top anchor.	4 / 5
Progressive Disclosure	SKILL.md is a clear overview with a task-routing table and a Reference Index pointing to 32 one-level-deep reference files (all verified to exist); content is appropriately split with high-value pitfalls inline and detailed specs externalized, matching the 'clear overview with well-signaled one-level-deep references' anchor.	5 / 5
	Total	18 / 20 Passed

Description

100%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.

An excellent description: concrete capabilities, comprehensive natural trigger phrases with synonyms, explicit what/when guidance, and clear disambiguation against the related dt-platform-costs skill. Every clause earns its place rather than padding.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('write, build, fix, or OPTIMIZE a DQL query', 'prevents syntax errors', 'makes queries faster, more efficient, and cheaper') plus a comprehensive coverage list (fetch commands, data models, field namespaces, time alignment, entity/smartscape patterns, metric discovery, optimization techniques), matching the 'comprehensive coverage' anchor.	5 / 5
Completeness	Explicitly answers what ('Core DQL syntax, pitfalls, query patterns, and query optimization') and when (a dedicated Trigger clause with concrete phrases), plus negative guidance, satisfying the 'clearly and explicitly answers both what AND when' anchor.	5 / 5
Trigger Term Quality	The explicit Trigger clause covers natural user phrases with synonyms — 'write/build/fix a DQL query', 'query logs/spans/metrics', 'create a timeseries', 'optimize my DQL', 'make my query faster/cheaper', 'reduce DQL cost/consumption/scanned data' — matching the comprehensive-synonyms anchor.	5 / 5
Distinctiveness Conflict Risk	Provides explicit conflict resolution ('Do NOT use to explain an existing query... For MONITORING a tenant's ACTUAL query consumption/billing... use dt-platform-costs — this tunes the query text, not billing data'), carving a clear niche with minimal conflict risk.	5 / 5
	Total	20 / 20 Passed

Validation

87%

Warnings & errors only

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

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 28 deeper-than-1-level	Warning
referenced_paths_exist	Referenced path issues: 31 deeper-than-1-level	Warning

	Total	14 / 16 Passed

Repository: Dynatrace/dynatrace-for-ai
Path: skills/dt-dql-essentials/SKILL.md
Commit: 3287f9d

Reviewed: 5 days ago

Table of Contents

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