dt-alerting
End-to-end Dynatrace alerting lifecycle — anomaly detector setup and model selection (static threshold, adaptive baseline,…
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,
$ npx -y skills add Dynatrace/dynatrace-for-ai --skill dt-dql-essentials --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/dt-dql-essentialsContext preview
The summary Claude sees to decide when to auto-load this skill.
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,
name: dt-dql-essentials description: "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." license: Apache-2.0
DQL is a pipeline-based query language. Queries chain commands with `|` to filter, transform, and aggregate data. DQL has unique syntax that differs from SQL — load this skill before writing any DQL query.
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Before working on specific tasks, load the relevant reference:
| Task | Required Reading | | ----------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | Discovering data | [references/discovery.md](references/discovery.md) | | Field names, namespaces, data models, stability levels, query patterns | [references/semantic-dictionary.md](references/semantic-dictionary.md) | | Query optimization — make a query faster / more efficient / cheaper, reduce consumption & scanned data (filter early, bucket filters, time ranges, field selection, sampling, cardinality) | [references/optimization.md](references/optimization.md) | | Smartscape topology navigation for discovering relationships between entities | [references/smartscape-topology-navigation.md](references/smartscape-topology-navigation.md) | | `summarize` and `makeTimeseries` patterns (bucketing, calendar months) | [references/summarization.md](references/summarization.md) | | Array and timeseries manipulation (`arrayFilter`, `collectArray`, iterative) | [references/iterative-expressions.md](references/iterative-expressions.md) | | Conditional logic (`if/else` chains), `coalesce`, string/date helpers | [references/useful-expressions.md](references/useful-expressions.md) | | `in` operator (subquery), full `@` time alignment unit table | [references/operators.md](references/operators.md) | | `matchesValue`, `matchesPhrase`, `matchesPattern`, `in()` — string pattern matching, regex, array matching, wildcards, case sensitivity | [references/string-matching.md](references/string-matching.md) |
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Use this index to route from a function group (e.g. time functions, conversions) to its detailed spec, or from a function name to its spec file.
| Description | Items | |-------------|-------| | [Data Types](references/dql/dql-data-types.md) | `array`, `binary`, `boolean`, `double`, `duration`, `long`, `record`, `string`, `timeframe`, `timestamp`, `uid` | | [Parameter Value Types](references/dql/dql-parameter-value-types.md) | `bucket`, `dataObject`, `dplPattern`, `entityAttribute`, `entitySelector`, `entityType`, `enum`, `executionBlock`, `expressionTimeseriesAggregation`, `expressionWithConstantValue`, `expressionWithFieldAccess`, `fieldPattern`, `filePattern`, `identifierForAnyField`, `identifierForEdgeType`, `identifierForFieldOnRootLevel`, `identifierForNodeType`, `joinCondition`, `jsonPath`, `metricKey`, `metricTimeseriesAggregation`, `namelessDplPattern`, `nonEmptyExecutionBlock`, `prefix`, `primitiveValue`, `simpleIdentifier`, `tabularFileExisting`, `tabularFileNew`, `url` | | [Commands](references/dql/dql-commands.md) | `append`, `data`, `dedup`, `describe`, `expand`, `fetch`, `fields`, `fieldsAdd`, `fieldsFlatten`, `fieldsKeep`, `fieldsRemove`, `fieldsRename`, `fieldsSnapshot`, `fieldsSummary`, `filter`, `filterOut`, `join`, `joinNested`, `limit`, `load`, `lookup`, `makeTimeseries`, `metrics`, `parse`, `search`, `smartscapeEdges`, `smartscapeNodes`, `sort`, `summarize`, `timeseries`, `traverse` | | [Functions — Aggregation](references/dql/dql-functions-aggregation.md) | `avg`, `collectArray`, `collectDistinct`, `correlation`, `count`, `countDistinct`, `countDistinctApprox`, `countDistinctExact`, `countIf`, `max`, `median`, `min`, `percentRank`, `percentile`, `percentileFromSamples`, `percentiles`, `stddev`, `sum`, `takeAny`, `takeFirst`, `takeLast`, `takeMax`, `takeMin`, `variance` | | [Functions — Array](references/dql/dql-functions-array.md) | `arrayAvg`, `arrayConcat`, `arrayCumulativeSum`, `arrayDelta`, `arrayDiff`, `arrayDistinct`, `arrayFirst`, `arrayFlatten`, `arrayIndexOf`, `arrayLast`, `arrayLastIndexOf`, `arrayMax`, `arrayMedian`, `arrayMin`, `arrayMovingAvg`, `arrayMovingMax`, `arrayMovingMin`, `arrayMovingSum`, `arrayPercentile`, `arrayRemoveNulls`, `arrayReverse`, `arraySize`, `arraySlice`, `arraySort`, `arraySum`, `arrayToString`, `vectorCosineDistance`, `vectorInnerProductDistance`, `vectorL1Distance`, `vectorL2Distance` | | [Functions — Bitwise](references/dql
Everything AI agents need to work with Dynatrace, starting with skills. Skills are portable knowledge packages following the Agent Skills open format. They give AI coding agents the domain-specific context to query, analyze, and interpret Dynatrace data.
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