From axiom-cli
Provides APL query reference for Axiom observability data with operators, functions, patterns, and CLI usage. Auto-invoked for writing or debugging queries.
How this skill is triggered — by the user, by Claude, or both
Slash command
/axiom-cli:axiom-aplThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
APL is Axiom's query language for analyzing observability data. This skill provides comprehensive guidance for writing, debugging, and optimizing APL queries.
APL is Axiom's query language for analyzing observability data. This skill provides comprehensive guidance for writing, debugging, and optimizing APL queries.
Documentation: https://axiom.co/docs/apl/introduction
CLI usage: See references/cli.md
axiom dataset list -f json
['<dataset>'] | getschema
Never guess field names. The schema shows all fields with their types.
['<dataset>'] | limit 10
See references for operators, functions, and patterns.
['dataset-name'] // Bracket notation (required for names with dots/dashes)
dataset_name // Plain identifier (only for simple names)
field_name // Plain field
['field.with.dots'] // Bracket notation for dotted fields
['service.name'] // OTel data (see references/otel.md for field mappings)
['dataset']
| where <condition>
| extend <new_field> = <expression>
| summarize <aggregation> by <grouping>
| project <fields>
| sort by <field> desc
| limit 100
Always filter by time first - it's the most selective filter.
// Relative time
| where _time >= ago(1h)
| where _time >= ago(24h) and _time < ago(1h)
// Absolute time
| where _time >= datetime(2024-01-15T10:00:00Z)
| where _time between (datetime(2024-01-15) .. datetime(2024-01-16))
Time functions:
ago(timespan) - Relative past timenow() - Current timedatetime(string) - Parse datetimebin(_time, 5m) - Time bucketingbin_auto(_time) - Automatic bucketinggetschema directly instead of invoking the full skillnpx claudepluginhub axiomhq/cli --plugin axiom-cliExplores an Axiom dataset to reveal its schema, fields, volume, and patterns. Use when discovering a new dataset or investigating data structure.
Generates LogQL queries, stream selectors, metric queries, and alerting rules for Grafana Loki via interactive workflow handling versions, labels, and use cases like debugging or dashboards.
Guides Honeycomb queries on trace/event datasets: percentiles over AVG, HEATMAP distributions, relational fields (root.,any.,none.), calculated fields, query math, result interpretation (P99/P50, heatmaps). For latency, errors, outliers, slow requests.