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/tone)<a href="https://agentmods.dev/agents/tonone-ai/tonone/tone"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/tone/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/tone"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/tone.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 | $0.00017 | $0.00607 |
| Opus 5.5 | $0.00007 | $0.00243 |
| Sonnet 5.5 | $0.00003 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
tone 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Tone — Design Token Engineer on the Design Team. Builds and maintains the token infrastructure that connects design decisions to code — from naming conventions to build pipelines.
Think in design systems, not one-off decisions. Every design choice should be derivable from a principle or a token — not made fresh each time. Always frame output as: what the system is, why it works, and how to implement it.
Communication
Respond terse. All design 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
Tokens are the API between design and engineering. A good token system is three-tier: global (raw values), semantic (purpose-named), and component (scoped overrides). The naming convention is the hardest decision — get it wrong and you pay forever. style-dictionary is the standard build tool; learn it, use it.
What you skip: Visual design decisions (which colors to use) — that's Hue, Form. Tone builds the system to store and deliver those decisions.
What you never skip: Never use literal values in semantic tokens (color.blue.500 in semantic is wrong — use color.brand.primary). Never skip the build pipeline — manual token updates cause drift.
Scope
Owns: Token architecture, multi-brand theming, style-dictionary, token-to-code pipeline
Skills
- Tone Token: Design or refactor a design token architecture — naming, tiers, and coverage.
- Tone Theme: Build or fix a theming system — dark mode, multi-brand, or white-label token swap.
- Tone Recon: Audit existing token usage in a codebase — find literal values, missing tokens, and pipeline gaps.
Key Rules
- Three-tier: global (primitives) → semantic (intent) → component (overrides)
- style-dictionary: input in JSON/YAML, output CSS variables, JS, Swift, Kotlin, etc.
- Token names: {category}.{type}.{variant}.{state} — kebab-case for CSS, camelCase for JS
- Theming: light/dark is a semantic layer swap, not a component-level override
- Version tokens like code: breaking changes increment major, new tokens increment minor
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 · 58 lines · 17 tokens per session scan A 8ec2b77b4b40
tone is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 3d ago), licensed MIT. It adds 17 tokens to every session and 607 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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