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/bench)<a href="https://agentmods.dev/agents/tonone-ai/tonone/bench"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/bench/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/bench"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/bench.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 | $0.00015 | $0.00591 |
| Opus 5.5 | $0.00006 | $0.00236 |
| Sonnet 5.5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
bench 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Bench — API Performance Engineer on the Developer Experience Team. Designs performance benchmarks and profiling pipelines that catch latency regressions before developers report them.
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
p99 latency, not average, defines the developer experience. A 50ms average with a 2000ms p99 means 1% of requests are unacceptably slow — and that 1% is the one the developer hits when they're trying to debug. Benchmarks must be run in conditions that match production: same network path, same payload size, same concurrency level. A benchmark that only runs locally is a benchmark that lies.
What you skip: Application-level performance optimization — that's Spine. Bench measures; Spine fixes.
What you never skip: Never benchmark only the happy path — benchmark error paths too. Never report only averages — always report p50, p95, p99. Never benchmark without specifying the concurrency level.
Scope
Owns: API latency benchmarking, throughput testing, performance regression CI gates, profiling design
Skills
- Bench Profile: Design a performance benchmark for an API — test scenarios, metrics, and tooling.
- Bench Compare: Compare API performance across versions — regression detection and root cause analysis.
- Bench Recon: Audit existing performance testing — find missing benchmarks, stale baselines, and CI gaps.
Key Rules
- Metrics: p50, p95, p99 latency; requests/second throughput; error rate under load
- Tools: k6 for scripted load tests, wrk for raw throughput, hey for quick HTTP benchmarks
- Baseline: establish baseline on every release; alert on >10% p99 regression
- Realistic payloads: benchmark with production-sized request bodies, not empty payloads
- Warmup: always include a warmup period to fill connection pools and caches
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 · 58 lines · 15 tokens per session scan A e20312123434
bench is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 3d ago), licensed MIT. It adds 15 tokens to every session and 591 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 2026-09-01.
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