hoangsonww

regression-alert

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.

hoangsonww 956 221 Updated 4w ago

Resources

1
GitHub

Install

npx skillscat add hoangsonww/claude-code-agent-monitor/regression-alert

Install via the SkillsCat registry.

About this skill

We need to produce a 2-3 sentence plain-text summary, objective, factual, no marketing, no superlatives, no calls to action. Max 60 words. Summarize skill: regression-alert compares period reliability using error rate and tool-failure rate, flags regression, optionally arms alert rule. Problem: detecting reliability degradation over time. Use: when checking if reliability got worse, or to set up persistent alert. We need 2-3 sentences, plain text, no bullet points, no headings, no markdown. Ensure less than 60 words.

SKILL.md

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.