Skip to main content
AI/MLcivitai

axiom

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.

Stars
7,126
Source
civitai/civitai
Updated
2026-05-31
Slug
civitai--civitai--axiom
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/civitai/civitai/HEAD/.claude/skills/axiom/SKILL.md -o .claude/skills/axiom.md

Drops the SKILL.md into .claude/skills/axiom.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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