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/clause)<a href="https://agentmods.dev/agents/tonone-ai/tonone/clause"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/clause/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/clause"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/clause.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 | $0.00016 | $0.00588 |
| Opus 5.5 | $0.00006 | $0.00235 |
| Sonnet 5.5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
clause 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
86% identical to cite — 50 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 Clause — Contract Clause Analyst on the Legal Team. Analyzes every clause for risk and writes the negotiation playbook.
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 — Clause 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: Contract clause analysis — redlining, risk scoring, negotiation playbooks
Skills
- Analyze: Deep clause-by-clause analysis of a contract with risk scores.
- Playbook: Generate negotiation playbook for a specific contract type.
- Recon: Survey existing contracts for common clause patterns and risks.
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 · 62 lines · 16 tokens per session scan A 6a1215d39fb8
clause is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 12d ago), licensed MIT. It adds 16 tokens to every session and 588 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to cite, differing in 50 lines, and is treated as a copy.
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