Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve. Use when projecting cost or asking "where will my spend land".
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Project where Claude Code spend will end up by the close of the current week or month.
The user provides: $ARGUMENTS
This is the forecast horizon — "week", "month", or a specific date. Default to
month (calendar month-end) when nothing is given, and state the horizon you used.
| Endpoint | Returns |
|---|---|
GET /api/analytics | { total_cost, tokens (effective totals, baselines pre-summed), daily_sessions (365d: [{ date, count }]), daily_events, overview, ... } — daily_sessions is the trend the forecast extrapolates |
GET /api/pricing/cost | { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — authoritative spend-to-date and avg cost-per-session input |
GET /api/sessions?limit=200 | Session list with inline cost and started_at — group by day for a sharper daily-spend curve than the count-based approximation |
Spend has no native per-day field, so build a daily-spend series and extrapolate:
total_cost from /api/pricing/cost.total_cost / total_session_count.daily_spend[d] ≈ daily_sessions[d].count × avg_cost_per_session. For a sharper curve, instead sum inline session cost grouped by DATE(started_at).avg_daily_spend = mean(daily_spend over the trailing 7 days). Also compute a 14-day average to gauge whether the trend is accelerating (▲) or cooling (▼).projected_total = spend_to_date_this_period + (avg_daily_spend × days_remaining).Spend-to-date this period: when the trend covers more than the current period, restrict the spend-to-date term to sessions whose
started_atfalls inside the current week/month so the projection isn't inflated by older spend.
total_cost, session count, avg cost/session, and how much falls inside the current period.
The 7-day and 14-day moving averages of daily spend, with a ▲/▼ accelerating-vs-cooling read. Show the last 7 days as a compact table (date, sessions, est. spend).
avg_daily_spend × days_remaining and the resulting projected_total for the horizon. State the days-remaining count explicitly.
If the user mentions a budget, show projected vs. budget, the over/under delta, and the date the budget is projected to be crossed (days_to_budget = (budget − spend_to_date) / avg_daily_spend).
Note that the forecast assumes the recent daily pace holds, that daily spend is approximated from session counts unless an inline-cost curve was used, and call out any low-data horizons (e.g. fewer than 7 active days).
Markdown with the trend table and the projection. Currency as USD to 4 decimal places; show moving averages and the projected total prominently. Deltas with ▲/▼.
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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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