Our review
This skill provides a comprehensive reference for Axiom's APL query language, enabling writing, debugging, and optimizing observability queries.
Strengths
- Comprehensive reference of APL operators and functions
- Advanced time handling with filters and binning
- Automatic dataset schema discovery
- Integration with Axiom CLI for execution
Limitations
- Requires authenticated Axiom CLI
- Does not cover undocumented edge cases
- May become outdated with Axiom updates
Use this skill when you need to analyze observability data with Axiom and require assistance writing or optimizing APL queries.
Do not use for simple field lookups (use getschema directly) or if you already have a working query.
Security analysis
SafeThe skill only uses read-only Axiom CLI commands (axiom dataset list, axiom query, axiom stream) to help construct APL queries. There are no destructive operations, external data exfiltration, or obfuscated code.
No concerns found
Examples
Write an APL query to find all error-level logs from the 'production' dataset in the last hour, group by service, and show the count per service.How can I create a time series of average trace duration bucketed by 5-minute intervals in Axiom APL?List the schema fields for the 'k8s-logs' dataset using Axiom APL.name: axiom-apl description: APL query language reference for Axiom. Provides operators, functions, patterns, and CLI usage. Auto-invoked by specialized Axiom skills when writing or debugging APL queries. compatibility: Requires authenticated Axiom CLI (axiom) user-invocable: false context: fork allowed-tools: Bash(axiom query:), Bash(axiom dataset list:), Bash(axiom stream:*), Read, Grep, Glob
Axiom Processing Language (APL)
APL is Axiom's query language for analyzing observability data. This skill provides comprehensive guidance for writing, debugging, and optimizing APL queries.
Quick Reference
Documentation: https://axiom.co/docs/apl/introduction
CLI usage: See references/cli.md
Core Workflow
1. List Available Datasets
axiom dataset list -f json
2. Discover Schema (CRITICAL - Always Do First)
['<dataset>'] | getschema
Never guess field names. The schema shows all fields with their types.
3. Sample Data
['<dataset>'] | limit 10
4. Write Query
See references for operators, functions, and patterns.
APL Syntax Essentials
Dataset Reference
['dataset-name'] // Bracket notation (required for names with dots/dashes)
dataset_name // Plain identifier (only for simple names)
Field Reference
field_name // Plain field
['field.with.dots'] // Bracket notation for dotted fields
['service.name'] // OTel data (see references/otel.md for field mappings)
Basic Query Structure
['dataset']
| where <condition>
| extend <new_field> = <expression>
| summarize <aggregation> by <grouping>
| project <fields>
| sort by <field> desc
| limit 100
Time Handling
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 bucketing
When NOT to Use
- Simple field lookup: Use
getschemadirectly instead of invoking the full skill - Known query patterns: If you already have a working query, don't re-invoke for syntax help
- Real-time alerting: Use Axiom Monitors for continuous alerting, not ad-hoc queries
References
- CLI Usage - Command flags and execution
- Operators - Tabular and scalar operators
- Functions - String, datetime, aggregation functions
- Patterns - Query patterns by use case
- Common Gotchas - Mistakes and fixes
- OpenTelemetry - OTel field mappings and trace patterns
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