# Spend Forecast

Forecast Claude Code spend to the end of the week or month from the daily session trend...

- Repository: https://github.com/hoangsonww/Claude-Code-Agent-Monitor (1136 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill spend-forecast -g -a claude-code -y`
Codex: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill spend-forecast -g -a codex -y`
Cursor: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill spend-forecast -g -a cursor -y`
Gemini CLI: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill spend-forecast -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add hoangsonww/Claude-Code-Agent-Monitor`

## 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".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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)`.

The rest: https://github.com/hoangsonww/Claude-Code-Agent-Monitor/blob/HEAD/plugins/ccam-cost-guard/skills/spend-forecast/SKILL.md