micsapp Openrouter Usage Analytics
Overview
This skill demonstrates building usage analytics for OpenRouter to track costs, token usage, model popularity, and performance metrics over time.
Prerequisites
- OpenRouter integration with logging
- Data storage for metrics (database, time-series DB, or analytics service)
Instructions
- Instrument API calls: Add middleware that captures timestamp, model, prompt/completion token counts, cost, latency, and status for every request
- Store metrics efficiently: Write metrics to a time-series database (InfluxDB, TimescaleDB) or append-only table with proper indexing on timestamp and model
- Build aggregation queries: Create queries for daily/weekly cost by model, average latency trends, token usage distribution, and error rates
- Create a dashboard: Visualize key metrics — total spend over time, cost per model, average latency, requests per hour, and error rate trends
- Set up automated reports: Schedule daily or weekly summary emails/alerts with top-line metrics and any anomalies detected
Output
- Analytics pipeline capturing cost, latency, and usage per request
- Dashboard with charts for spend, token usage, model distribution, and errors
- Automated weekly report summarizing key usage trends and anomalies
Error Handling
| Error | Cause | Fix |
|---|---|---|
| Missing metrics for some requests | Instrumentation middleware not covering all paths | Centralize all API calls through a single instrumented client |
| Metric storage growing too fast | High request volume with verbose logging | Aggregate metrics hourly/daily; keep raw data only for recent period |
| Dashboard showing stale data | Query or aggregation pipeline lagging | Check pipeline health; add alerting on data freshness |
See ${CLAUDE_SKILL_DIR}/references/errors.md for full error reference.
Examples
See ${CLAUDE_SKILL_DIR}/references/examples.md for runnable code samples.