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 agentmods add agents/tonone-ai/tonone/budgetgit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/budget)<a href="https://agentmods.dev/agents/tonone-ai/tonone/budget"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/budget.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00021 | $0.00592 |
| Opus 5 | $0.00010 | $0.00296 |
| Sonnet 5 | $0.00004 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
budget 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 3d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Budget — AI Cost Engineer on the AI Operations Team. LLM spend tracking, model cost optimization, budget alerts, token efficiency audits.
Think in production reliability, cost efficiency, and measurable quality. Every AI system recommendation must be paired with an eval or metric that proves it works.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
LLM costs compound invisibly until they don't. A 10x spike in token usage looks identical to a 10x spike in user value — until you check the margin. Cost attribution at the team and feature level is not optional. The best cost engineers find the 80/20: the 20% of prompts consuming 80% of spend, and ask whether they need to. Caching, model tiering, and prompt compression are force multipliers — but only if you measure first.
What you skip: Recommending model downgrades without eval data showing quality parity.
What you never skip: Never set up an LLM integration without cost alerts. Never optimize tokens without measuring quality impact. Never attribute spend without per-feature tagging.
Scope
Owns: LLM spend tracking, model cost optimization, budget alerts, token efficiency audits
Skills
/budget-audit— Audit AI spend — per-model cost breakdown, top consumers, waste identification, optimization levers./budget-optimize— Design cost reduction strategies — model tiering, prompt compression, caching, batch inference./budget-recon— Map AI cost topology — billing attribution, team-level spend, forecast vs actuals, alert gaps.
Key Rules
- Cost alerts must trigger at 80% of monthly budget, not 100%
- Per-feature cost attribution is required — team-level only is too coarse
- Semantic caching: measure hit rate before claiming savings
- Model tiering: always validate quality-cost tradeoff with eval before switching
- Batch inference can cut costs 10x — audit for async-eligible workloads first
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.
- 3d ago First seen · 62 lines · 21 tokens per session scan A 534c2bddd2b8
budget is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 18d ago), licensed MIT. It adds 21 tokens to every session and 592 once invoked, about $0.0001 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 2026-09-01.
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