Search a Claude Code session transcript for a string or regex pattern and show every matching message with surrounding context. Reads /api/sessions/:id/transcript and resolves sessions via /api/sessions?limit= from the Agent Monitor dashboard. Use when hunting for a specific message, prompt, tool call, or error inside a session's conversation.
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Find where a pattern appears in a session transcript and show the matches in context.
The user provides: $ARGUMENTS
Interpreted as a session reference plus a search pattern, e.g.
<session-id> "rate limit" or latest TypeError. Parsing rules:
latest/last (most recently updated session).| Endpoint | Returns |
|---|---|
GET /api/sessions?limit=N | session list to resolve latest/last and to confirm the id exists |
GET /api/sessions/:id/transcript | the ordered transcript messages (role, content, tool calls/results, timestamps) for the session |
If latest/last (or no id), call GET /api/sessions?limit=1. Otherwise verify
the id with GET /api/sessions?limit=1000 (or GET /api/sessions/:id). Report
the resolved id, status, and model before searching.
Call GET /api/sessions/:id/transcript. Walk the messages in order and match the
pattern against message text, tool_name, and tool input/output content.
Case-insensitive by default; treat the pattern as a regex if it contains regex
metacharacters, otherwise as a literal substring.
For each match show:
[#N HH:MM:SS role(:tool_name)]
… preceding line of context …
> matching line with the **pattern** emphasized
… following line of context …
Number matches sequentially. Include ±1–2 messages (or lines) of context so the match is interpretable. If a tool call matches, show the tool_name and a trimmed view of its arguments/result.
Report: total matches, how many distinct messages matched, the roles involved (user / assistant / tool), and the timestamp span of the matches. If there are zero matches, say so plainly and suggest a looser pattern.
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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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