Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-patterns.
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Identify recurring patterns using the Agent Monitor's workflow intelligence engine.
The user provides: $ARGUMENTS
Options: "all", "tools", "errors", "workflows", "last N sessions".
| Endpoint | Returns |
|---|---|
GET /api/sessions?limit=200 | Session list with status, model, cwd, metadata |
GET /api/analytics | tool_usage top 20, event_types, agent_types |
GET /api/workflows/{sessionId} | 11 datasets per session (see below) |
| Dataset | Pattern insight |
|---|---|
toolFlow | Tool transition matrix: tool A → tool B with counts — reveals sequential habits |
patterns | Detected workflow patterns: recurring sequences with frequency scores |
cooccurrence | Agent co-occurrence: which agents frequently run together |
modelDelegation | Model habits: which models are chosen for which task types |
errorPropagation | Error patterns: where errors start and how they cascade by agent depth |
effectiveness | Subagent patterns: which types succeed most, avg duration per type |
compaction | Compaction triggers: what causes context overflow |
complexity | Complexity patterns: session complexity scores over time |
toolFlow)patterns)errorPropagation + event_types)cooccurrence + effectiveness)daily_sessions)Pattern Report with top 10 patterns ranked by frequency × impact:
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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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