Claude Code Agent Monitor is a dashboard that records and displays activity from Claude Code and Codex coding-agent sessions, including tool use and subagent coordination. It is for people who need live visibility into agent work through a web interface or native desktop app. The catalogue entries extend Claude Code with skills, commands, agents, plugins, instructions, hooks, and settings related to the monitor.
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 5.5 | $0.00033 | $0.00350 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (1,121 stars, last pushed 4d ago), licensed MIT. It adds 83 tokens to every session and 875 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
laravel-stripe-connect
Use when implementing multi-vendor marketplace payments, seller onboarding, commissions, payouts, or split payments with Stripe Connect.
finance-analysis
Financial analysis, modeling, FP&A, investment research, and tax strategy. Use when the user asks about three-statement models, DCF valuation, variance analysis, budgeting, rolling forecasts, equity research, due diligence, portfolio analysis, GAAP/IFRS accounting, month-end close, multi-jurisdictional tax…
real-estate-mortgage
Residential real estate transactions and mortgage lending workflows. Use when the user asks about buyer/seller representation, offers, transaction coordination, mortgage borrower intake, TRID compliance, loan pipeline tracking, closing coordination, MLS, escrow, title, or appraisal contingencies. Triggers on real…
financial-statement-analysis
Reads a set of financial statements and establishes what changed and why — fluctuation analysis against prior period and against budget, profitability, liquidity, solvency and efficiency ratios, benchmarking, and the non-GAAP measures presented alongside them. Use this to interpret results, review a counterparty's or…
collections-email
Write a polite-but-firm payment-reminder / collections email sequence for overdue invoices. Use when asked to write a collections email, a payment reminder, a dunning sequence, or to chase an overdue invoice. Produces a staged sequence — gentle pre-due nudge through escalating overdue reminders to a final notice …
cash-flow-forecaster
Forecast project cash flow based on schedule and cost data. Generate S-curves and payment projections.