
Regression Alert
Compare this period's reliability against the prior period using Agent Monitor data, ...
Install
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill regression-alert -g -a claude-code -y- Repository
- hoangsonww/Claude-Code-Agent-Monitor1.1k
- Category
- AI and agents
- Updated
- 5d ago
- License
- MIT
- Context cost
- Lightabout 796 tokens
- Runs scripts
- No, instructions only
What it does
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
APIErrorevents.

