| name | explore-dataset |
| description | Explore an Axiom dataset to understand its schema, fields, volume, and patterns. Use when discovering a new dataset, investigating data structure, or understanding what data is available. |
| compatibility | Requires authenticated Axiom CLI (axiom) |
| user-invocable | true |
| context | fork |
| allowed-tools | Bash(axiom query *), Bash(axiom dataset list), Bash(axiom dataset list *), Bash(axiom config get *), Read, Grep, Glob |
Dataset Exploration
Systematically explore an Axiom dataset to understand its structure, content, and potential use cases.
Arguments
When invoked with a dataset name (e.g., /explore-dataset logs), the name is available as $ARGUMENTS.
Exploration Protocol
1. List Available Datasets
If no dataset specified, list what's available:
axiom dataset list -f json
2. Schema Discovery
Always start here. Discover actual field names and types:
axiom query "['<dataset>'] | getschema" --start-time -1h
Identify:
- Field names and types
- Dotted fields requiring bracket notation
- Timestamp fields
- Key dimensions (service, status, level)
OTel trace data: If schema contains trace_id, span_id, attributes.*, note that:
- Service fields are promoted: use
['service.name'] not ['resource.service.name']
- Custom attributes:
['attributes.custom']['field'] with tostring() for aggregations
- See
axiom-apl skill's OTel reference for field mappings
3. Sample Data
Examine actual values:
axiom query "['<dataset>'] | limit 10" --start-time -1h -f json
Look for:
- Data structure and relationships
- Field value formats
- Data quality issues
4. Volume Analysis
Understand data volume patterns:
axiom query "['<dataset>'] | summarize count() by bin(_time, 1h) | sort by _time asc" --start-time -24h
Analyze:
- Event volume over time
- Data freshness
- Collection gaps
5. Categorical Field Analysis
For each key categorical field (status, level, service):
axiom query "['<dataset>'] | summarize count() by <field> | top 20 by count_" --start-time -1h
Identify:
- Value distributions
- Cardinality
- Key dimensions for filtering
6. Numerical Field Statistics
For numeric fields (duration, bytes, count):
axiom query "['<dataset>'] | summarize count(), min(<field>), max(<field>), avg(<field>), percentiles(<field>, 50, 95, 99)" --start-time -1h
7. Error Pattern Detection
Search for error indicators:
axiom query "search in (['<dataset>']) 'error' or 'fail' or 'exception' | limit 20" --start-time -1h
Output Format
Provide a summary including:
## Dataset Summary: <name>
### Purpose
<What system generated this data, what it represents>
### Key Fields
| Field | Type | Description |
|-------|------|-------------|
| ... | ... | ... |
### Volume
- Events per hour: ~X
- Data freshness: last event at X
### Key Dimensions
- `status`: 200, 400, 500, ...
- `service.name`: api, web, worker, ...
### Recommended Queries
<Common queries for this dataset>
### Monitoring Opportunities
<What could be alerted on>
When NOT to Use
- Known datasets: If you already understand the schema, skip exploration and query directly
- Quick field check: Use
getschema directly for single field lookups
- Production queries: Exploration uses expensive operations (
search); extract patterns then optimize
- Repeated analysis: Once explored, document findings and reuse—don't re-explore
APL Reference
For query syntax, invoke the axiom-apl skill which provides comprehensive documentation on operators, functions, and patterns.
