Regression Watch

Detect whether Claude Code sessions are getting worse over time across quality and
efficiency metrics, using Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

• empty or "all" — check every regression metric (default)
• "errors" — error-rate regression only
• "cache" — cache hit-rate regression only
• "compaction" — compaction-frequency regression only
• "cost" — cost-per-session regression only
• A window like "last 30d" or "30 vs 90" — set the recent vs baseline window sizes

Data Sources

Endpoint	Returns
GET /api/analytics	daily_events (365d), daily_sessions (365d), event_types, tokens (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), avg_events_per_session
GET /api/events?session_id=X	Event stream incl. APIError, Compaction, PreToolUse/PostToolUse — used to localize regressions to specific sessions
GET /api/pricing/cost	{ total_cost, breakdown[...] } — total cost to derive cost-per-session
GET /api/pricing/cost/{sessionId}	Per-session cost — used to compare recent vs baseline session cost
GET /api/workflows/{sessionId}	compaction (impact), errorPropagation (by depth), effectiveness — per-session quality signals
GET /api/sessions?limit=N	Sessions with started_at, cost, metadata — to bucket sessions into time windows

Report Sections

1. Windowing

Split history into a baseline window (older) and a recent window (newer).
Default: recent = last 30 days, baseline = the 30–90 day range before it. Use
daily_events/daily_sessions for series metrics and GET /api/sessions?limit=N
to assign sessions to each window by started_at.

2. Error Rate Regression

• Recent error rate = APIError count / total events in the recent window
  (from event_types and daily_events, or per-session GET /api/events).
• Compare to the baseline rate. Flag if recent is higher.
• Report the absolute and relative change and which sessions contributed most
  APIError events.

3. Cache Hit Rate Regression

• Cache hit rate = total_cache_read / (total_cache_read + total_input).
• Compute for each window (per-window input/cache_read from session metadata or
  the pricing breakdown). Flag a falling hit rate — that means more
  uncached input tokens and higher cost.

4. Compaction Frequency Regression

• Compaction frequency = Compaction events / session per window (from
  event_types / daily_events, confirmed via per-session
  GET /api/workflows/{id} compaction). Flag a rising rate — context is
  overflowing more often.

5. Cost-per-Session Regression

• Cost-per-session = window total cost / window session count, using
  GET /api/pricing/cost overall and GET /api/pricing/cost/{id} for the
  sessions in each window. Flag a climbing value.

6. Verdict

Roll up which metrics regressed, rank by relative worsening, and name the most
likely driver (e.g., cache hit rate fell → cost per session climbed).

Output

• A Markdown table: metric | baseline | recent | Δ | direction (▲ worse / ▼ better) | verdict.
• Tag each regressed metric 🔴 (clear regression), 🟡 (mild/within noise), or 🟢 (improved).
• Currency in USD to 4 decimals; rates as percentages to 2 decimals.
• List the specific session IDs that contributed most to any regression.
• End with the single highest-priority regression to address and a concrete next step.
• Read-only: only report what the API returns; never fabricate baselines.