Walk a Claude Code session transcript turn-by-turn from Agent Monitor data, summarizing each user, assistant, and tool message in order so a long conversation can be reviewed quickly. Anchors the recap to the session header (model, cost, turn_count). Use when reviewing what was actually said and done in a conversation.
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Replay a session transcript one turn at a time with a concise summary of each message.
The user provides: $ARGUMENTS
1-10) or errors to focus on tool failures.| Endpoint | Returns |
|---|---|
GET /api/sessions/:id/transcript | ordered transcript messages: role (user / assistant / tool), content, tool_name and tool result where applicable, timestamps |
GET /api/sessions/:id | session header: status, model, cwd, started_at, ended_at, cost, metadata (thinking_blocks, turn_count, total_turn_duration_ms) |
GET /api/sessions/:id. Print id, model, cwd basename, status, turn_count,
thinking_blocks, total_turn_duration_ms, and cost in one block.
GET /api/sessions/:id/transcript. Iterate messages in order. For each turn emit
one compact entry:
Group an assistant message with the tool calls it triggered so each "turn" reads as intent → action → result.
After the walk, pull out: the original goal, the key turning points, any tool failures or retries, and how the session ended (resolved / errored / abandoned).
A numbered turn list (Turn N — <role>: <one-line summary>), grouped intent →
action → result, preceded by the header block and followed by the highlights.
Truncate any quoted content past ~200 chars with …. Currency as USD to 4 decimal
places. Summarize faithfully — never invent message content that is not in the
transcript. If the transcript endpoint returns empty, say the session has no stored
transcript (it may predate transcript capture or need a reimport) rather than
fabricating turns. If the dashboard is unreachable, tell the user to start it with
npm start from the repo root.
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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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