Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.
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Generate a month-over-month productivity retrospective from Agent Monitor data.
The user provides: $ARGUMENTS
This may be:
The comparison period is always the immediately preceding calendar month.
| Endpoint | Returns |
|---|---|
GET /api/analytics | daily_sessions and daily_events (365d) for monthly bucketing and trends; tokens (total_input/output/cache_read/cache_write — baselines pre-summed); tool_usage (top 20); sessions_by_status |
GET /api/sessions?limit=500 | Sessions with started_at, ended_at, status, model, cwd, cost, and metadata (turn_count, thinking_blocks) — for per-project (cwd) grouping and completion rate |
GET /api/pricing/cost | total_cost and per-model breakdown (input/output/cache tokens, cost, matched_rule) |
Compare the target month to the prior month in a table:
| Metric | This Month | Last Month | Change |
|---|---|---|---|
| Sessions | N | N | ▲/▼ N% |
| Total Cost | $X.XXXX | $X.XXXX | ▲/▼ N% |
| Tokens (in/out/cache) | N | N | ▲/▼ N% |
| Completion Rate | N% | N% | ▲/▼ N pts |
| Active Days | N | N | ▲/▼ |
Derive monthly buckets from daily_sessions / daily_events. Completion rate =
completed sessions / total sessions for the month (from sessions_by_status and
the filtered session list).
Group the month's sessions by cwd. For the top 5–8 projects, list session count,
total cost, completion rate, and dominant model. Note any project that newly
appeared or dropped off versus last month.
From /api/pricing/cost, show cost per model and the dominant token type. Compute
cache hit rate = total_cache_read / (total_cache_read + total_input) and compare
to last month. Currency to 4 decimals.
From tool_usage, highlight the tools that rose or fell most month-over-month, and
any new tool adopted. Flag rising error/Compaction activity if present.
Three to five plain-language observations: what changed, why it likely changed, and what it implies (e.g., "cost up 22% but sessions flat → heavier per-session work").
Two to four prioritized, actionable goals grounded in the numbers above.
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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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