explore-dataset
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.
What this skill does
# 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: ```bash axiom dataset list -f json ``` ### 2. Schema Discovery **Always start here.** Discover actual field names and types: ```bash 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](../axiom-apl/references/otel.md) for field mappings ### 3. Sample Data Examine actual values: ```bash 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: ```bash 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): ```bash 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): ```bash 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: ```bash axiom query "search in (['<dataset>']) 'error' or 'fail' or 'exception' | limit 20" --start-time -1h ``` ## Output Format Provide a summary including: ```markdown ## 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.
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