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name: spend-forecast description: > 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".

Spend Forecast

Project where Claude Code spend will end up by the close of the current week or month.

Input

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.

Data Sources

| 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 |

Forecast method

Spend has no native per-day field, so build a daily-spend series and extrapolate:

1. Spend-to-date = total_cost from /api/pricing/cost.
2. Avg cost per session = total_cost / total_session_count.
3. Daily spend series: for the trailing window, 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).
4. Moving average: 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 (▼).
5. Remaining days: days left until the end of the chosen horizon (week = through Sunday; month = through the last calendar day).
6. Projection: 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_at falls inside the current week/month so the projection isn't inflated by older spend.

Report Sections

1. Spend to date

total_cost, session count, avg cost/session, and how much falls inside the current period.

2. Daily trend

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).

3. Projection

avg_daily_spend × days_remaining and the resulting projected_total for the horizon. State the days-remaining count explicitly.

4. Budget check (if a budget is known)

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).

5. Confidence & caveats

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).

Output

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 ▲/▼.