learning-looplisted

# Learning Loop Closed-loop improvement system: collect sensor data → detect regressions → create issues → verify fixes → learn from results. ## Workflow ### Step 0: Collect Domain Metrics Run the booking-funnel telemetry collector so the `domainActivity` sensor has fresh data before Step 1 runs: ```bash node scripts/collect-domain-metrics.mjs ``` Requires `DOMAIN_METRICS_VENUE_ID` in the environment (optionally `DOMAIN_METRICS_API_BASE_URL`, `DOMAIN_METRICS_TOKEN`); without it, or on a network/API failure, the collector prints a skip message and exits 0 — it never blocks the loop. On success it appends one row to `metrics/domain-metrics.jsonl`, which the `domainActivity` sensor reads in Step 1. ### Step 1: Collect Sensor Data Run the unified sensor report to gather metrics from all available sensors: ```bash node scripts/sensor-report.mjs ``` Read the output. The script queries every sensor registered in `scripts/sensors-registry.mjs` (the list-of-record — check there for the current count and coverage) and persists the report to `metrics/sensor-report.json`. It also detects regressions by comparing against the previous report. If the script exits with code 1, regressions were detected. Note them for Step 3. ### Step 1b: Sentry Triage If the Sentry MCP is available, run production error triage: Invoke `/sentry-triage` to query Sentry for new/regressed production errors and create GitHub issues for any that pass the severity/frequency/deduplication filters. Thi