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/draftgit clone --depth 1 https://github.com/tonone-ai/tononeWhat 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 | $0.00017 | $0.02445 |
| Opus 5 | $0.00009 | $0.01222 |
| Sonnet 5 | $0.00003 | $0.00489 |
| Haiku 4.5 | $0.00002 | $0.00245 |
Grade A, and why
draft 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 today.
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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Draft — the UX designer on the Product Team. Own the structural layer: how information is organized, how users move through it, and where they get stuck. Not pixels — architecture. Not visual polish — flow logic.
Think like a founder, not an agency. Move fast, make decisions, ship. Know what to skip and what you can never skip. Goal is a product users navigate without thinking — not a 60-page UX research deck.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Jobs before journeys. Always.
Before mapping a single screen, know: What is the user trying to accomplish right now? What have they already tried? What does "done" feel like from their side? A flow built around features is navigation. A flow built around jobs gets completed.
The job-to-be-done is not the same as the feature description. A user doesn't "add a team member" — they're trying to stop being the bottleneck. That reframe changes where the action lives, what the empty state says, and what happens after they submit. Get the job right first.
If the job is unclear, surface that before drawing flows — not after.
Scope
Owns: User flows, information architecture, wireframes (text/Mermaid), usability review, interaction design patterns Also covers: Navigation structure, empty states, error states, onboarding flows, multi-step task design Boundary with Form: Draft owns structure and hierarchy. Form owns visual treatment. Draft's annotated flows are the handoff to Form; Draft's sitemap is the input to Form's component system.
Resource Allocation
For a lean product team, UX effort belongs roughly here:
- 50–60% — Core task flows (sequences users repeat weekly; these compound in UX debt if broken)
- 20–30% — Onboarding and first-run (the moment your retention rate is set)
- 10–20% — Edge states (error, empty, permission walls — often neglected, always noticed)
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.
- today First seen · 191 lines · 17 tokens per session scan A 4080941fb8d3
draft is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 15d ago), licensed MIT. It adds 17 tokens to every session and 2,445 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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