par_cc_usage

Monitors and analyzes Claude Code usage in real-time. Helps operations teams track API calls, usage patterns, and costs. Integrates with CLI and terminal workflows for easy deployment.

[{"step":"Install the par_cc_usage CLI tool by running `pip install par-cc-usage` or `brew install par-cc-usage`. Ensure you have API access credentials configured in your environment.","tip":"Use `par_cc_usage --setup` to configure default tracking parameters like cost thresholds or alert channels."},{"step":"Run a baseline scan to establish normal usage patterns. Execute `par_cc_usage --baseline --days 7` to generate a 7-day report. Compare this against real-time data to identify anomalies.","tip":"Store the baseline report in your team's documentation (e.g., Notion or Confluence) for future reference."},{"step":"Set up real-time monitoring with `par_cc_usage --monitor --interval 5m`. This will track usage every 5 minutes and alert you to spikes via Slack, email, or webhook.","tip":"Configure alerts in `par_cc_usage --config alerts.json` to notify specific team members (e.g., DevOps or team leads) based on usage thresholds."},{"step":"Generate actionable reports for stakeholders. Use `par_cc_usage --report --team engineering --format markdown` to create a summary for your engineering team. Export raw data with `par_cc_usage --export --format csv` for finance or auditing.","tip":"Schedule automated reports with cron (e.g., `0 9 * * 1-5 par_cc_usage --report --team engineering`) to share weekly updates."},{"step":"Optimize usage based on recommendations. Use the `par_cc_usage --optimize --project [PROJECT_NAME]` command to get project-specific suggestions, or manually review the exported data in tools like Google Sheets or Tableau.","tip":"Combine par_cc_usage with Claude Code's built-in logging (e.g., `claude --log-level debug`) for deeper debugging of high-usage scenarios."}]