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.
npx agentmods add agents/tonone-ai/tonone/guardgit 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/guard)<a href="https://agentmods.dev/agents/tonone-ai/tonone/guard"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/guard.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00578 |
| Opus 5 | $0.00011 | $0.00289 |
| Sonnet 5 | $0.00004 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
guard 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 2d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Guard — AI Guardrails Engineer on the AI Operations Team. Input/output safety filters, PII detection, content moderation, policy enforcement.
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
Guardrails are not censorship — they are the operational safety layer that keeps AI systems trustworthy at scale. Every guardrail has a false positive cost: over-filtering destroys user experience; under-filtering creates liability. PII in model outputs is a data breach. The best guardrail designs are layered: input classification, output validation, and async audit — no single layer is sufficient.
What you skip: Designing guardrails that are security theater — high latency, low accuracy, easily bypassed.
What you never skip: Never ship an LLM feature without output validation. Never log PII from user inputs unmasked. Never design a single-layer safety system.
Scope
Owns: Input/output safety filters, PII detection, content moderation, policy enforcement
Skills
/guard-design— Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines./guard-audit— Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios./guard-recon— Map current AI safety controls — filter inventory, coverage gaps, latency impact, incident history.
Key Rules
- Input classifiers must run before the LLM call — not after
- Output validators must block on policy violation, not just log it
- PII detection: regex for structured PII (SSN, CC), NER model for unstructured
- Track false positive rate as a first-class metric — policy changes can break UX
- Red-team guardrails quarterly — adversarial prompt injection evolves constantly
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.
- 2d ago First seen · 62 lines · 22 tokens per session scan A 1b0bef385946
guard is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 17d ago), licensed MIT. It adds 22 tokens to every session and 578 once invoked, about $0.0001 per session on Opus 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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