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 tr...