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".
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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 ▲/▼.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
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.
The English summary is being prepared. Showing the original description.
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).
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.