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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Score a session against the rolling population average and report its percentile on cost, tokens, tool count, and complexity using Agent Monitor data.
The user provides: $ARGUMENTS
This may be:
| Endpoint | Returns |
|---|---|
GET /api/sessions?limit=N | Population of sessions with cost, model, started_at, metadata (turn_count, total_turn_duration_ms) — builds the rolling baseline |
GET /api/pricing/cost/{sessionId} | { total_cost, breakdown:[{ input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost }] } — the target session's cost and tokens |
GET /api/workflows/{sessionId} | complexity (score), stats (tool/event counts), toolFlow (distinct tools used) — the target session's tool count and complexity |
GET /api/analytics | avg_events_per_session, tool_usage, daily_sessions — corroborates population-level averages |
Fetch the population with GET /api/sessions?limit=200 (the rolling set). For each
session gather cost (GET /api/pricing/cost/{id} or the list cost field), total
tokens (sum of the 4 token types from the pricing breakdown), tool count and
complexity (GET /api/workflows/{id}). Compute mean, median, and standard
deviation for each metric across the population.
For the requested session, pull the same four metrics:
total_cost from GET /api/pricing/cost/{id}.input + output + cache_read + cache_write summed from the breakdown.GET /api/workflows/{id} stats/toolFlow.complexity.score from GET /api/workflows/{id}.For each metric report the target's percentile within the population (share of
sessions at or below it) and its z-score (value − mean) / stddev. Label each:
below average / typical / above average / outlier (|z| > 2).
State whether the session was normal overall. If it is an outlier, name which metric drove it (e.g., complexity p96, cost p91 → an unusually heavy session).
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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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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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Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.
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