axiom

DevOps & Security

Query Axiom logs and datasets using APL (Axiom Processing Language). Use when investigating production errors, debugging webhook failures, checking log patterns, or analyzing system behavior.

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Source SKILL.md: https://github.com/civitai/civitai/blob/HEAD/.claude/skills/axiom/SKILL.md

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Axiom

Query Axiom datasets using APL (Axiom Processing Language). Supports log search, aggregation, field analysis, and time-based filtering.

Setup

Create .env in this skill directory:

AXIOM_TOKEN=xaat-your-token-here
AXIOM_ORG_ID=civitai-cxe5
AXIOM_DATASTREAM=civitai-prod
AXIOM_DOMAIN=api.axiom.co

Token needs read/query permissions (the project .env token is ingest-only).

Quick Reference

SKILL_DIR=".claude/skills/axiom"

# List available datasets
node "$SKILL_DIR/axiom.mjs" datasets

# Run an APL query
node "$SKILL_DIR/axiom.mjs" query "['civitai-prod'] | where name == 'nowpayments-webhook' | take 10" --format legacy --json

# Search logs with filters
node "$SKILL_DIR/axiom.mjs" query "['civitai-prod'] | where name == 'some-service' and type == 'error' | project _time, message, error | sort by _time desc | take 50" --start "2026-03-01T00:00:00Z" --end "2026-04-01T00:00:00Z" --format legacy --json

# Count errors by message
node "$SKILL_DIR/axiom.mjs" query "['civitai-prod'] | where name == 'my-service' | summarize count() by message | order by count_ desc" --start "2026-03-01T00:00:00Z" --format legacy --json

# Top values for a field
node "$SKILL_DIR/axiom.mjs" query "['civitai-prod'] | where type == 'error' | summarize count() by name | order by count_ desc | take 20" --format legacy --json

Commands

Command	Description
`datasets`	List all available datasets
`dataset-info <name>`	Get info about a specific dataset
`query "<APL>"`	Run any APL query
`search <dataset> --where "..."`	Search with filters
`count <dataset> --where "..." --by <field>`	Count/aggregate
`tail <dataset>`	Most recent events
`top <dataset> <field>`	Top values for a field

Important Notes

Use --format legacy --json for reliable output. The tabular format can return empty rows for some queries.
Time ranges: Use --start and --end flags with ISO 8601 timestamps, or use ago() in APL (e.g., _time > ago(24h)).
Field paths: Log data is under the data. prefix in legacy format. Use field names directly in APL (e.g., name, message, type).
Summarize queries: Return results in buckets.series[].groups[] in legacy format, not in matches[].

Known Datasets

Dataset	Description
`civitai-prod`	Main production logs (services, webhooks, jobs)
`civitai-stage-new`	Staging environment
`civitai-next`	Next.js application logs
`webhooks`	Webhook event tracking
`clickhouse`	ClickHouse integration errors
`notifications`	Notification service logs
`orchestration-otlp`	Orchestration telemetry
`python-worker`	Python worker process logs

Common Log Names (civitai-prod)

Services log with a name field. Common ones:

nowpayments-webhook — NowPayments IPN webhook handler
nowpayments-service — NowPayments deposit processing
reconcile-nowpayments-job — Reconciliation cron job

APL Cheatsheet

# Filter
| where name == "value"
| where field contains "substring"
| where field matches regex "pattern"

# Time range
| where _time > ago(7d)
| where _time between (datetime(2026-03-01) .. datetime(2026-03-31))

# Aggregate
| summarize count() by field
| summarize avg(duration), max(duration) by name
| summarize count() by bin(_time, 1h)

# Sort and limit
| sort by _time desc
| take 50
| order by count_ desc

# Select fields
| project _time, name, message, error

# Extend (computed columns)
| extend duration_ms = duration / 1000

When to Use

Investigating production errors or webhook failures
Checking if a specific service is logging errors
Analyzing error patterns over time
Debugging payment/deposit processing issues
Monitoring reconciliation job health
Verifying deployment behavior changes