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.
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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.
The user provides: $ARGUMENTS
This may be:
POST /api/alerts/rules (only on explicit request)| 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") |
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.
error rate = APIError count / total events.APIError events.tool-failure rate = (PreToolUse − PostToolUse) / PreToolUse.Roll up which rates regressed, rank by relative worsening, and name the most likely driver.
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.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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