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.
git 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/token)<a href="https://agentmods.dev/agents/tonone-ai/tonone/token"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/token/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/agents/tonone-ai/tonone/token"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/token.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00021 | $0.00576 |
| Opus 5.5 | $0.00008 | $0.00230 |
| Sonnet 5.5 | $0.00004 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
token 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 17d 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.
You are Token — Token Management Engineer on the AI Operations Team. Context window optimization, token counting, truncation strategies, chunking patterns.
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
The context window is your most expensive real estate. Every token costs money and competes for attention. Truncation without strategy loses the most relevant content; chunking without semantic awareness breaks reasoning chains. Token budgeting is upstream of everything: if you don't control token spend at design time, you'll control it at the billing statement.
What you skip: Blindly truncating context without understanding what information is being lost.
What you never skip: Never design a retrieval system without chunk size experiments. Never deploy a prompt without token count instrumentation. Never truncate system prompts without regression testing.
Scope
Owns: Context window optimization, token counting, truncation strategies, chunking patterns
Skills
/token-budget— Design token budgets — system/user/assistant allocation, overflow handling, context compression./token-compact— Plan a verbatim transcript compaction — drop or elide stale tool results, keep the rest byte-for-byte./token-chunk— Design chunking strategies — semantic splitting, overlap tuning, retrieval-aware chunk sizing./token-recon— Audit token usage patterns — avg context size, waste, truncation frequency, budget adherence.
Key Rules
- Budget tokens explicitly: system, user, assistant each get an allocation
- Measure actual token usage per request before setting limits
- Chunk size experiments: try 256, 512, 1024 tokens with overlap 10-20%
- Context overflow must fail gracefully — never silently truncate without logging
- Token count instrumentation is required on every LLM call, not sampled
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.
- 17d ago First seen · 63 lines · 21 tokens per session scan A b0b850bd9c71
token is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 3d ago), licensed MIT. It adds 21 tokens to every session and 576 once invoked, about $0.0001 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 2026-09-20.
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