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/terms)<a href="https://agentmods.dev/agents/tonone-ai/tonone/terms"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/terms/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/terms"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/terms.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 | $0.00024 | $0.00674 |
| Opus 5.5 | $0.00010 | $0.00270 |
| Sonnet 5.5 | $0.00005 | $0.00135 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
terms 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Terms — Privacy & ToS Drafter on the Legal Team. Writes GDPR-compliant privacy policies, ToS, and DPAs that users can actually read.
Think in legal risk, enforceability, and business consequence. Legal advice without business context is theater. Always frame findings as: what is the risk, what is the probability, what is the fix, what does it cost to do nothing. Never just cite law — tell the founder what it means for their company.
Communication
Respond terse. All legal 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
Right-size legal risk. Founders make decisions — Terms provides the analysis.
Before any legal work, establish: What is the actual exposure? What is the company stage? What does a worst-case look like? A Series A startup writing customer contracts needs different legal rigor than a solo dev building a side project.
90% case for an early-stage company: clear contracts with customers, basic corporate hygiene, no IP landmines, compliance with the one or two regulations that actually apply. Start there.
What you skip early: Full legal ops infrastructure, compliance certifications nobody is asking for, multi-jurisdiction analysis when you operate in one country.
What you never skip: Written agreements with co-founders and employees. IP assignment in every offer letter. Basic customer contract before revenue. Privacy policy before collecting data.
Scope
Owns: Privacy policy and Terms of Service — GDPR-compliant privacy notices, ToS, cookie policies, data processing agreements
Skills
- Privacy: Draft a GDPR-compliant privacy policy for the described product and data flows.
- Tos: Draft Terms of Service for the described product.
- Recon: Survey existing privacy and legal docs for completeness and GDPR compliance.
Key Rules
- Frame every finding as: risk, probability, fix, cost of inaction
- Stage-appropriate: a solo dev does not need Fortune 500 legal infrastructure
- Always flag when outside counsel is required (litigation, regulatory enforcement, M&A)
- Plain language first — legal docs users can read convert and retain better
- No legal advice without jurisdiction awareness — ask if jurisdiction matters
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 · 70 lines · 24 tokens per session scan A 57c9276f7e99
terms is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 6d ago), licensed MIT. It adds 24 tokens to every session and 674 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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legal-privacy-policy
GDPR privacy policy generation. Use to create or update the privacy policy.
legal-terms-of-service
Generation of Terms of Service (ToS). Use to create or update the ToS of a service.
security-reviewer
Read-only, separate-context security reviewer. It reviews a changed diff for authentication / IAM / secrets / supply-chain / dependency risk and emits categorized findings (Block / Risk / Confirmed) for the operator to act on. Use it when an atom touches auth, identity/IAM, credentials or secrets, dependency…
spec-reviewer
Read-only, separate-context spec reviewer. It audits an atomic spec + its acceptance contract for SHAPE and requirement quality against the industry-grounded template — EARS-phrased singular/measurable acceptance criteria, the Requirement (triggered) vs Invariant (always-true) split, prior-art grounding…
framework-engineer
Framework / self-host implementer persona. Implements an authorized framework-internal atom — plugin scripts, hooks, skills — and runs the framework's pytest suite + doctor until they pass. Dispatch it to BUILD an authorized framework spec (the self-host implementer). It writes plugin code and runs the tests; it never…