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name: regression-alert description: > Compare this period's reliability against the prior period using Agent Monitor data — error rate (APIError/total) and tool-failure rate (PreToolUse→PostToolUse gap) — flag any regression where reliability got worse, and optionally wire a persistent alert rule so the dashboard catches the next regression automatically. Use when checking whether reliability degraded.

Regression Alert

Detect whether Claude Code reliability is getting worse period-over-period, and optionally arm an alert so it never has to be checked by hand again. Scope is reliability/failures only — for cache/cost/compaction drift, use ccam-insights' regression-watch instead.

Input

The user provides: $ARGUMENTS

This may be:

• empty or "all" — check error rate and tool-failure rate (default)
• "errors" — APIError-rate regression only
• "tools" — tool-failure-rate regression only
• a window like "7 vs 7" or "30 vs 30" — recent vs baseline window sizes (default: last 7 days vs the prior 7)
• "arm" — after reporting, also create an alert rule via POST /api/alerts/rules (only on explicit request)

Data Sources

Endpoint	Returns
GET /api/analytics	daily_events (365d), daily_sessions (365d), event_types — split into recent vs baseline windows to compute per-window failure rates
GET /api/events?session_id=X	Per-session stream — localize a regression to the sessions driving it
GET /api/alerts/rules	Existing alert rules — check whether a matching reliability rule already exists before arming a new one
POST /api/alerts/rules	Create a new alert rule (only when the user says "arm")

Report Sections

1. Windowing

Split history into a recent window (newer) and a baseline window (the equal-length period just before it). Default: recent = last 7 days, baseline = the prior 7. Use daily_events/daily_sessions to bucket counts by day.

2. Error-Rate Regression

• Per window: error rate = APIError count / total events.
• Compare recent vs baseline. Flag if recent is higher. Report absolute change (pp) and relative change (%), plus the recent sessions contributing the most APIError events.

3. Tool-Failure-Rate Regression

• Per window: tool-failure rate = (PreToolUse − PostToolUse) / PreToolUse.
• Compare recent vs baseline. Flag a rising rate as a reliability regression. Name the tools whose gap grew most.

4. Verdict

Roll up which rates regressed, rank by relative worsening, and name the most likely driver.

5. Optional — Arm an Alert

Only if the user passed "arm". First GET /api/alerts/rules to avoid duplicates. Then POST /api/alerts/rules with a rule that fires when the regressed metric crosses a threshold near the recent value (e.g., error rate > recent rate). Echo the created rule back; do not create webhooks or fire alerts.

Output

• A Markdown table: metric | baseline | recent | Δ (pp) | Δ (%) | direction (▲ worse / ▼ better) | verdict.
• Tag each metric 🔴 (clear regression), 🟡 (within noise), or 🟢 (improved).
• Rates as percentages to 2 decimals; any currency in USD to 4 decimals.
• List the specific session IDs that contributed most to any regression.
• End with the single highest-priority regression and a concrete next step (and, if armed, the new rule's id/threshold).
• Read-only except the explicit "arm" path, which is the only write. Never mutate alert rules otherwise. If curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.