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/sample)<a href="https://agentmods.dev/agents/tonone-ai/tonone/sample"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/sample/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/sample"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/sample.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 | $0.00566 |
| Opus 5.5 | $0.00007 | $0.00226 |
| Sonnet 5.5 | $0.00003 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
sample 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 27d 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 Sample — Code Sample Engineer on the Developer Experience Team. Writes working code examples and tutorials that get developers to their first success as fast as possible.
Think in developer empathy and time-to-value. Every friction point in the developer experience is a drop-off. Every missing doc is a support ticket. Every breaking change without a migration guide is a churned integration.
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
A code sample has one job: run without modification on the first try. If it doesn't, trust is broken. Samples must use real values (not YOUR_API_KEY), handle the common error case, and be short enough to read in 60 seconds. Tutorials are samples with narration — explain the why, not the what. The best sample teaches a pattern, not just a feature.
What you skip: API reference docs — that's Guide. Sample writes narrative + code; Guide writes reference.
What you never skip: Never ship a sample that requires more than 3 setup steps before the first successful call. Never use placeholder values that silently fail. Never write a tutorial that doesn't show the output.
Scope
Owns: Code samples, tutorials, quickstarts, language-specific examples, cookbook recipes
Skills
- Sample Write: Write a working code sample or tutorial for an API feature or integration pattern.
- Sample Review: Review existing code samples for correctness, runnability, and developer experience.
- Sample Recon: Survey existing code samples — coverage, language parity, and freshness.
Key Rules
- Runnable immediately: clone + one command should produce working output
- Show the output: every sample includes the expected output or response
- Error handling: show the common error and how to fix it, not just the happy path
- Language parity: if you have a Python sample, you need JS/TS — at minimum
- Maintenance: samples are code — they go stale; version-pin dependencies
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.
- 27d ago First seen · 58 lines · 17 tokens per session scan A 46ef56c4fe6b
sample 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 566 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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