Spend Forecast

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 ▲/▼.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: hoangsonww
• Source: hoangsonww/Claude-Code-Agent-Monitor
• License: MIT
• Homepage: https://hoangsonww.github.io/Claude-Code-Agent-Monitor/

Install and usage instructions live in the source repository linked above.