Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill spend-forecastgit clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-MonitorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/spend-forecast)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/spend-forecast"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/spend-forecast/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/spend-forecast"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/spend-forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.00875 |
| Opus 5 | $0.00042 | $0.00438 |
| Sonnet 5 | $0.00017 | $0.00175 |
| Haiku 4.5 | $0.00008 | $0.00088 |
Grade A, and why
spend-forecast scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Spend-to-date =
total_costfrom/api/pricing/cost. - Avg cost per session =
total_cost / total_session_count. - 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 sessioncostgrouped byDATE(started_at). - 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 (▼). - Remaining days: days left until the end of the chosen horizon (week = through Sunday; month = through the last calendar day).
- 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_atfalls inside the current week/month so the projection isn't inflated by older spend.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 63 lines · 83 tokens per session scan A 9d4e8fc49eee
spend-forecast is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (991 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 875 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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