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explore-dataset

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by axiomhq · part of axiomhq/cli

Explore an Axiom dataset to understand its schema, fields, volume, and patterns. Use when discovering a new dataset, investigating data structure, or…

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🧰 Not standalone. This skill ships with axiomhq/cli and only works together with that tool — install the tool first, then add this skill.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

by axiomhq

Explore an Axiom dataset to understand its schema, fields, volume, and patterns. Use when discovering a new dataset, investigating data structure, or… npx skills add https://github.com/axiomhq/cli --skill explore-dataset Download ZIPGitHub58

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 "[' '] | 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 "[' '] | 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 "[' '] | 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 "[' '] | summarize count() by | 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 "[' '] | summarize count(), min( ), max( ), avg( ), percentiles( , 50, 95, 99)" --start-time -1h

7. Error Pattern Detection

Search for error indicators:

axiom query "search in ([' ']) 'error' or 'fail' or 'exception' | limit 20" --start-time -1h

Output Format

Provide a summary including:


## Dataset Summary:

### Purpose
 

### 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
 

### Monitoring Opportunities

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.