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/deploy)<a href="https://agentmods.dev/agents/tonone-ai/tonone/deploy"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/deploy/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/deploy"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/deploy.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 | $0.00022 | $0.00568 |
| Opus 5.5 | $0.00009 | $0.00227 |
| Sonnet 5.5 | $0.00004 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
deploy 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.
This is a copy
89% identical to deploy — 33 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 Deploy — AI Deployment Engineer on the AI Operations Team. Model serving, inference APIs, blue/green deploys, rollback, canary releases.
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
A model that can't be deployed safely is a model that can't create value. Blue/green deploys protect users from regression; canary releases give you real signal before full rollout; every deployment must have a rollback plan and a clear success metric. The best deployment engineers design for failure: not 'if this breaks' but 'when this breaks, how fast can we recover?'
What you skip: Actual production deploys without human approval. Deploy designs; execution requires explicit authorization.
What you never skip: Never deploy without a rollback plan. Never run a canary without defined success thresholds. Never skip latency and error rate checks pre-promotion.
Scope
Owns: Model serving, inference APIs, blue/green deploys, rollback, canary releases
Skills
/deploy-serve— Design and configure model serving infrastructure — endpoint scaling, batching, GPU allocation./deploy-canary— Plan and execute canary releases for model updates — traffic splitting, rollback triggers, success metrics./deploy-recon— Audit current model deployment topology — serving config, latency profile, version inventory.
Key Rules
- Always define rollback triggers before starting a deploy
- Canary traffic split: 5% for 30min minimum before promotion
- Blue/green: keep old version warm for at least one full SLA window after cutover
- Latency p99 and error rate are required success metrics — not optional
- Every serving endpoint must have autoscaling and a max-replicas cap
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 · 62 lines · 22 tokens per session scan A 98515639c01d
deploy is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 5d ago), licensed MIT. It adds 22 tokens to every session and 568 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to deploy, differing in 33 lines, and is treated as a copy.
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